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mongodb/agent-skills
Use the official MongoDB Skills with your favorite coding agent to build faster.
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
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skills/mongodb-query-optimizer/SKILL.md
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skills/mongodb-search-and-ai/SKILL.md
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tools/review-skill/SKILL.md
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View on GitHubAdd to my skills
Verified commit 155bb9c4b42a9a3e019f47be0669de0e4547d1ea
git clone --filter=blob:none --no-checkout https://github.com/mongodb/agent-skills.git .hotskills/mongodb-agent-skills && git -C .hotskills/mongodb-agent-skills checkout --detach 155bb9c4b42a9a3e019f47be0669de0e4547d1ea
# Copy each selected skill directory from .hotskills/mongodb-agent-skills into .claude/skills/<skill-name>/git clone --filter=blob:none --no-checkout https://github.com/mongodb/agent-skills.git .hotskills/mongodb-agent-skills && git -C .hotskills/mongodb-agent-skills checkout --detach 155bb9c4b42a9a3e019f47be0669de0e4547d1ea
# Copy each selected skill directory from .hotskills/mongodb-agent-skills into .gemini/skills/<skill-name>/195GitHub stars
1star in 7 days
24skills in this repository
7commits in 30 days
Heat
2.9- Star growth, last 7 daysNot available
- Star growth, last 24 hoursNot available
- Rank movement, last 7 daysNot available
- Commits, last 30 daysNot available
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekNot available
Heat never changes the order by stars, and sponsorship never touches it. How heat works
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30-day change not yet available: not enough history.
Individual skills in this repository
- mongodb-atlas-stream-processing
plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing - mongodb-connection
plugins/mongodb-atlas/skills/mongodb-connection - mongodb-natural-language-querying
plugins/mongodb-atlas/skills/mongodb-natural-language-querying - mongodb-qe-size-estimation
plugins/mongodb-atlas/skills/mongodb-qe-size-estimation - mongodb-query-optimizer
plugins/mongodb-atlas/skills/mongodb-query-optimizer - mongodb-schema-design
plugins/mongodb-atlas/skills/mongodb-schema-design - mongodb-search-and-ai
plugins/mongodb-atlas/skills/mongodb-search-and-ai - mongodb-atlas-stream-processing
plugins/mongodb/skills/mongodb-atlas-stream-processing - mongodb-connection
plugins/mongodb/skills/mongodb-connection - mongodb-mcp-setup
plugins/mongodb/skills/mongodb-mcp-setup - mongodb-natural-language-querying
plugins/mongodb/skills/mongodb-natural-language-querying - mongodb-qe-size-estimation
plugins/mongodb/skills/mongodb-qe-size-estimation - mongodb-query-optimizer
plugins/mongodb/skills/mongodb-query-optimizer - mongodb-schema-design
plugins/mongodb/skills/mongodb-schema-design - mongodb-search-and-ai
plugins/mongodb/skills/mongodb-search-and-ai - mongodb-atlas-stream-processing
skills/mongodb-atlas-stream-processing - mongodb-connection
skills/mongodb-connection - mongodb-mcp-setup
skills/mongodb-mcp-setup - mongodb-natural-language-querying
skills/mongodb-natural-language-querying - mongodb-qe-size-estimation
skills/mongodb-qe-size-estimation - mongodb-query-optimizer
skills/mongodb-query-optimizer - mongodb-schema-design
skills/mongodb-schema-design - mongodb-search-and-ai
skills/mongodb-search-and-ai - review-skill
tools/review-skill
How it works
mongodb-atlas-stream-processing
When your agent uses it
Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
4,681 tokensWhen the skill is activated
Reference documents
202,434 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- If MCP tools are unavailable
- Tool Selection Matrix
- CRITICAL: Validate Before Creating Processors
- Pipeline Rules & Warnings
- MCP Tool Behaviors
- Connection Capabilities — Source/Sink Reference
- Core Workflows
- Pre-Deploy & Post-Deploy Checklists
- Tier Sizing & Performance
- Troubleshooting
- Billing & Cost
- Safety Rules
- Reference Files
Scripts executed separately
../mongodb-search-and-ai/scripts/quickstart_complete.py../../../mongodb/skills/mongodb-search-and-ai/scripts/quickstart_complete.py
Files
../../../../.claude-plugin/marketplace.jsonasset442 bytes../../.claude-plugin/plugin.jsonasset1,268 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesreferences/connection-configs.mddoc13,152 bytesreferences/development-workflow.mddoc15,383 bytesreferences/mcp-troubleshooting.mddoc1,824 bytesreferences/output-diagnostics.mddoc6,749 bytesreferences/pipeline-patterns.mddoc16,447 bytesreferences/sizing-and-parallelism.mddoc6,267 bytes../mongodb-connection/SKILL.mddoc13,926 bytes../mongodb-connection/references/monitoring-guide.mddoc8,567 bytes../mongodb-natural-language-querying/SKILL.mddoc9,630 bytes../mongodb-qe-size-estimation/SKILL.mddoc16,946 bytes../mongodb-query-optimizer/SKILL.mddoc10,331 bytes../mongodb-query-optimizer/references/aggregation-optimization.mddoc6,721 bytes../mongodb-query-optimizer/references/antipattern-examples.mddoc2,527 bytes../mongodb-query-optimizer/references/core-indexing-principles.mddoc4,319 bytes../mongodb-query-optimizer/references/update-query-examples.mddoc1,144 bytes../mongodb-schema-design/SKILL.mddoc13,647 bytes../mongodb-schema-design/references/antipattern-excessive-lookups.mddoc3,608 bytes../mongodb-schema-design/references/antipattern-unnecessary-collections.mddoc4,194 bytes../mongodb-schema-design/references/antipattern-unnecessary-indexes.mddoc3,773 bytes../mongodb-schema-design/references/fundamental-document-model.mddoc3,608 bytes../mongodb-schema-design/references/fundamental-document-size.mddoc9,803 bytes../mongodb-schema-design/references/fundamental-embed-vs-reference.mddoc15,238 bytes../mongodb-schema-design/references/fundamental-schema-validation.mddoc4,382 bytes../mongodb-schema-design/references/pattern-approximation.mddoc3,310 bytes../mongodb-schema-design/references/pattern-archive.mddoc5,456 bytes../mongodb-schema-design/references/pattern-attribute.mddoc2,485 bytes../mongodb-schema-design/references/pattern-bucket.mddoc4,268 bytes../mongodb-schema-design/references/pattern-computed.mddoc6,163 bytes../mongodb-schema-design/references/pattern-document-versioning.mddoc5,143 bytes../mongodb-schema-design/references/pattern-extended-reference.mddoc3,403 bytes../mongodb-schema-design/references/pattern-outlier.mddoc5,760 bytes../mongodb-schema-design/references/pattern-polymorphic.mddoc6,115 bytes../mongodb-schema-design/references/pattern-schema-versioning.mddoc8,419 bytes../mongodb-schema-design/references/pattern-time-series-collections.mddoc6,611 bytes../mongodb-schema-design/references/source-query-stats.mddoc3,431 bytes../mongodb-schema-design/references/source-slow-query-logs.mddoc2,345 bytes../mongodb-search-and-ai/SKILL.mddoc8,900 bytes../mongodb-search-and-ai/references/automated-embedding.mddoc16,231 bytes../mongodb-search-and-ai/references/hybrid-search.mddoc23,667 bytes../mongodb-search-and-ai/references/lexical-search-indexing.mddoc19,995 bytes../mongodb-search-and-ai/references/lexical-search-querying.mddoc17,878 bytes../mongodb-search-and-ai/references/quick-start.mddoc46,196 bytes../mongodb-search-and-ai/references/vector-search.mddoc19,133 bytes../mongodb-search-and-ai/scripts/quickstart_complete.pyruns13,883 bytes../../../mongodb/.claude-plugin/plugin.jsonasset906 bytes../../../mongodb/mcp.jsonasset119 bytes../../../mongodb/skills/mongodb-atlas-stream-processing/SKILL.mddoc19,851 bytes../../../mongodb/skills/mongodb-atlas-stream-processing/references/connection-configs.mddoc13,152 bytes../../../mongodb/skills/mongodb-atlas-stream-processing/references/development-workflow.mddoc15,383 bytes../../../mongodb/skills/mongodb-atlas-stream-processing/references/mcp-troubleshooting.mddoc1,824 bytes../../../mongodb/skills/mongodb-atlas-stream-processing/references/output-diagnostics.mddoc6,749 bytes../../../mongodb/skills/mongodb-atlas-stream-processing/references/pipeline-patterns.mddoc16,447 bytes../../../mongodb/skills/mongodb-atlas-stream-processing/references/sizing-and-parallelism.mddoc6,267 bytes../../../mongodb/skills/mongodb-connection/SKILL.mddoc13,926 bytes../../../mongodb/skills/mongodb-connection/references/monitoring-guide.mddoc8,567 bytes../../../mongodb/skills/mongodb-mcp-setup/SKILL.mddoc13,319 bytes../../../mongodb/skills/mongodb-natural-language-querying/SKILL.mddoc9,630 bytes../../../mongodb/skills/mongodb-qe-size-estimation/SKILL.mddoc16,946 bytes../../../mongodb/skills/mongodb-query-optimizer/SKILL.mddoc10,331 bytes../../../mongodb/skills/mongodb-query-optimizer/references/aggregation-optimization.mddoc6,721 bytes../../../mongodb/skills/mongodb-query-optimizer/references/antipattern-examples.mddoc2,527 bytes../../../mongodb/skills/mongodb-query-optimizer/references/core-indexing-principles.mddoc4,319 bytes../../../mongodb/skills/mongodb-query-optimizer/references/update-query-examples.mddoc1,144 bytes../../../mongodb/skills/mongodb-schema-design/SKILL.mddoc13,647 bytes../../../mongodb/skills/mongodb-schema-design/references/antipattern-excessive-lookups.mddoc3,608 bytes../../../mongodb/skills/mongodb-schema-design/references/antipattern-unnecessary-collections.mddoc4,194 bytes../../../mongodb/skills/mongodb-schema-design/references/antipattern-unnecessary-indexes.mddoc3,773 bytes../../../mongodb/skills/mongodb-schema-design/references/fundamental-document-model.mddoc3,608 bytes../../../mongodb/skills/mongodb-schema-design/references/fundamental-document-size.mddoc9,803 bytes../../../mongodb/skills/mongodb-schema-design/references/fundamental-embed-vs-reference.mddoc15,238 bytes../../../mongodb/skills/mongodb-schema-design/references/fundamental-schema-validation.mddoc4,382 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-approximation.mddoc3,310 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-archive.mddoc5,456 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-attribute.mddoc2,485 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-bucket.mddoc4,268 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-computed.mddoc6,163 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-document-versioning.mddoc5,143 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-extended-reference.mddoc3,403 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-outlier.mddoc5,760 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-polymorphic.mddoc6,115 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-schema-versioning.mddoc8,419 bytes../../../mongodb/skills/mongodb-schema-design/references/pattern-time-series-collections.mddoc6,611 bytes../../../mongodb/skills/mongodb-schema-design/references/source-query-stats.mddoc3,431 bytes../../../mongodb/skills/mongodb-schema-design/references/source-slow-query-logs.mddoc2,345 bytes../../../mongodb/skills/mongodb-search-and-ai/SKILL.mddoc8,900 bytes../../../mongodb/skills/mongodb-search-and-ai/references/automated-embedding.mddoc16,231 bytes../../../mongodb/skills/mongodb-search-and-ai/references/hybrid-search.mddoc23,667 bytes../../../mongodb/skills/mongodb-search-and-ai/references/lexical-search-indexing.mddoc19,995 bytes../../../mongodb/skills/mongodb-search-and-ai/references/lexical-search-querying.mddoc17,878 bytes../../../mongodb/skills/mongodb-search-and-ai/references/quick-start.mddoc46,196 bytes../../../mongodb/skills/mongodb-search-and-ai/references/vector-search.mddoc19,133 bytes../../../mongodb/skills/mongodb-search-and-ai/scripts/quickstart_complete.pyruns13,883 bytes
Read SKILL.md
# MongoDB Atlas Streams
Build, operate, and debug Atlas Stream Processing (ASP) pipelines using four MCP tools from the MongoDB MCP Server.
## Prerequisites
This skill requires the **MongoDB MCP Server** connected with:
- Atlas API credentials (`apiClientId` and `apiClientSecret`)
The 4 tools: `atlas-streams-discover`, `atlas-streams-build`, `atlas-streams-manage`, `atlas-streams-teardown`.
**All operations require an Atlas project ID.** If unknown, call `atlas-list-projects` first to find your project ID.
## If MCP tools are unavailablemongodb-connection
When your agent uses it
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
What it loads into context
Frontmatter
146 tokensAlways available to the agent
SKILL.md
2,844 tokensWhen the skill is activated
Reference documents
1,784 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Principle: Context Before Configuration
- Understanding How Connection Pools Work
- Configuration Design
- Troubleshooting Connection Issues
- Environmental Context (MANDATORY)
- Advising on Monitoring & Iteration
- When creating code
Files
SKILL.mdcore13,926 bytesSKILL.mdcore13,926 bytesreferences/monitoring-guide.mddoc8,567 bytes
Read SKILL.md
# MongoDB Connection Optimizer
You are an expert in MongoDB connection management across all officially supported driver languages (Node.js, Python, Java, Go, C#, Ruby, PHP, etc.). Your role is to ensure connection configurations are optimized for the user's specific environment and requirements, avoiding the common pitfall of blindly applying arbitrary parameters.
## Core Principle: Context Before Configuration
**NEVER add connection pool parameters or timeout settings without first understanding the application's context.** Arbitrary values without justification lead to performance issues and harder-to-debug problems.
## Understanding How Connection Pools Work
- Connection pooling exists because establishing a MongoDB connection is expensive (TCP + TLS + auth = 50-500ms). Without pooling, every operation pays this cost.
- Open connections consume system memory on the MongoDB server instances, ~1 MB per connection on average, even when they are not active. It is advised to avoid having idle connections.
**Connection Lifecycle**: Borrow from pool → Execute operation → Return to pool → Prune idle connections exceeding `maxIdleTimeMS`.mongodb-natural-language-querying
When your agent uses it
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
What it loads into context
Frontmatter
195 tokensAlways available to the agent
SKILL.md
2,210 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Query Generation Process
- Best Practices
- Schema Analysis
- Sample Document Usage
- Error Handling
- Example Workflow
- Managing Context Size
Files
SKILL.mdcore9,630 bytes
Read SKILL.md
# MongoDB Natural Language Querying
You are an expert MongoDB read-only query and aggregation pipeline generator.
## Query Generation Process
### 1. Gather Context Using MCP Tools
**Required Information:**
- Database name and collection name (use `mcp__mongodb__list-databases` and `mcp__mongodb__list-collections` if not provided)
- User's natural language description of the query
**Fetch in this order:**
mongodb-qe-size-estimation
When your agent uses it
Estimates the storage and memory impact of encrypting fields in collections with Queryable Encryption (QE) enabled. Do NOT use for collections that use Client-Side Field Level Encryption (CSFLE) instead of QE.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
3,842 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- General Instructions
- 1. Prepare the Working Directory
- 2. State Purpose and Request Input Preference
- 3. Get the Number of Documents
- 4. Get the Number of Entries
- 5. Get Inputs Per Field
- 6. Add Formulas and Results to qe-sizing-calculations.md
- 7. Calculate Index Storage, Total Disk Storage, and Memory
- 8. Sum Total Results and Report Conclusions
- 9. Interpret Results
Files
SKILL.mdcore16,946 bytes
Read SKILL.md
# mongodb-qe-size-estimation
## General Instructions
- **CRITICAL: Never ask for or accept sample data.** QE is an encryption feature, meant to secure sensitive information. Do not request sample data, and if provided, reject it for security reasons. Inform the user that you can't accept sample documents, though you can take an encryption schema as an input to see which fields are encrypted, which allow queries, and what those query settings are.
- Any parenthetical in the form (LLM Note: <content>) is for LLM use. Don't save it to qe-sizing-calculations.md or mention it to the user.
- Tone: Be brief, not conversational. Don't restate information from prior exchanges unless a step specifically says to.
- Don't write anything to the file except what's specified in the steps.
- This skill relies on a question-answer-flow to get all required inputs. For Steps 2-5, as long as the user is providing valid input, your responses should include both acknowledgment of the input, and the next question.
- The user may try to keep things moving by inputting multiple inputs. If inputs don't clearly map to a field and its configuration, ask for clarification. Otherwise validate them, and accept them if valid.
- This skill currently has no formula for range queries, so while it accepts "range" query configuration for compatible BSON types, it uses 0 for estimated values.
### Definitionsmongodb-query-optimizer
When your agent uses it
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
What it loads into context
Frontmatter
182 tokensAlways available to the agent
SKILL.md
2,390 tokensWhen the skill is activated
Reference documents
3,766 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When this skill is invoked
- High Level Workflow
- MCP: available tools
- Example workflow 1 (help with specific query)
- Example workflow 2 (general database performance help)
- Load references
- Output
Files
SKILL.mdcore10,331 bytesreferences/aggregation-optimization.mddoc6,721 bytesreferences/antipattern-examples.mddoc2,527 bytesreferences/core-indexing-principles.mddoc4,319 bytesreferences/update-query-examples.mddoc1,144 bytes
Read SKILL.md
# MongoDB Query Optimizer
## When this skill is invoked
Invoke **only** when the user wants:
- Query/index **optimization** or **performance** help
- **Why** a query is slow or **how to speed it up**
- **Slow queries** on their cluster and/or **how to optimize them**
Do **not** invoke for routine query authoring unless the user has requested help with optimization, slow queries, or indexing.
## High Level Workflow
mongodb-schema-design
When your agent uses it
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
What it loads into context
Frontmatter
151 tokensAlways available to the agent
SKILL.md
2,823 tokensWhen the skill is activated
Reference documents
26,044 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Apply
- Quick Reference
- Key Principle
- Embed/Reference Decision Framework
- How to Use
- How These Rules Work
Files
SKILL.mdcore13,647 bytesSKILL.mdcore13,647 bytesSKILL.mdcore13,647 bytesreferences/antipattern-excessive-lookups.mddoc3,608 bytesreferences/antipattern-unnecessary-collections.mddoc4,194 bytesreferences/antipattern-unnecessary-indexes.mddoc3,773 bytesreferences/fundamental-document-model.mddoc3,608 bytesreferences/fundamental-document-size.mddoc9,803 bytesreferences/fundamental-embed-vs-reference.mddoc15,238 bytesreferences/fundamental-schema-validation.mddoc4,382 bytesreferences/pattern-approximation.mddoc3,310 bytesreferences/pattern-archive.mddoc5,456 bytesreferences/pattern-attribute.mddoc2,485 bytesreferences/pattern-bucket.mddoc4,268 bytesreferences/pattern-computed.mddoc6,163 bytesreferences/pattern-document-versioning.mddoc5,143 bytesreferences/pattern-extended-reference.mddoc3,403 bytesreferences/pattern-outlier.mddoc5,760 bytesreferences/pattern-polymorphic.mddoc6,115 bytesreferences/pattern-schema-versioning.mddoc8,419 bytesreferences/pattern-time-series-collections.mddoc6,611 bytesreferences/source-query-stats.mddoc3,431 bytesreferences/source-slow-query-logs.mddoc2,345 bytes
Read SKILL.md
# MongoDB Schema Design
Data modeling patterns and anti-patterns for MongoDB, maintained by MongoDB. Bad schema is the root cause of most MongoDB performance and cost issues—queries and indexes cannot fix a fundamentally wrong model.
## When to Apply
Reference these guidelines when:
- Designing a new MongoDB schema from scratch
- Migrating from SQL/relational databases to MongoDB
- Reviewing existing data models for performance issues
- Troubleshooting slow queries or growing document sizes
- Deciding between embedding and referencing
- Modeling relationships (one-to-one, one-to-many, many-to-many)
- Implementing tree/hierarchical structuresmongodb-search-and-ai
When your agent uses it
Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search (semantic), and Hybrid Search. Use this skill for ANY question about MongoDB search, including open-ended and explanatory ones asked before the user has any data or use case — 'how does search work in MongoDB?', 'I'm new to MongoDB search, where do I start?', 'what's the difference between Atlas Search and Vector Search?', 'which search type should I use?', 'what is vector search?', 'how does automated embedding work?', 'can you explain hybrid search?'. Do not answer these from general knowledge; use this skill. Also use to build search features: full-text queries, autocomplete, fuzzy matching, faceted search, semantic similarity, embeddings, RAG, text containment or substring matching ('contains', 'includes'), case-insensitive or multi-field text search, and filtering across many fields. Provides workflows for choosing a search type, creating indexes, writing queries, and optimizing performance via the MongoDB MCP server.
What it loads into context
Frontmatter
235 tokensAlways available to the agent
SKILL.md
1,890 tokensWhen the skill is activated
Reference documents
34,552 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Principles
- Workflow
- Anti-Patterns to Avoid
- Handling Edge Cases
Scripts executed separately
scripts/quickstart_complete.py
Files
SKILL.mdcore8,900 bytesreferences/automated-embedding.mddoc16,231 bytesreferences/hybrid-search.mddoc23,667 bytesreferences/lexical-search-indexing.mddoc19,995 bytesreferences/lexical-search-querying.mddoc17,878 bytesreferences/quick-start.mddoc46,196 bytesreferences/vector-search.mddoc19,133 bytesscripts/quickstart_complete.pyruns13,883 bytes
Read SKILL.md
# MongoDB Search and AI Recommendations Skill
You are helping MongoDB users implement, optimize, and troubleshoot Atlas Search (lexical), Vector Search (semantic), and Hybrid Search (combined) solutions.
## Core Principles
1. **Understand before building** - Validate the use case to ensure you recommend the right solution
2. **Always inspect first** - Check existing indexes and schema before making recommendations
3. **Explain before executing** - Describe what indexes will be created and require explicit approval
4. **Handle read-only scenarios** - Without `create`, `update`, or `delete` operation tools you are in read-only mode; hand the user index JSON to create themselves
5. **Explain in accessible language** - Describe technical concepts and map business requirements to technical implementations in terms the user can follow
## Workflow
mongodb-atlas-stream-processing
When your agent uses it
Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
4,681 tokensWhen the skill is activated
Reference documents
15,115 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- If MCP tools are unavailable
- Tool Selection Matrix
- CRITICAL: Validate Before Creating Processors
- Pipeline Rules & Warnings
- MCP Tool Behaviors
- Connection Capabilities — Source/Sink Reference
- Core Workflows
- Pre-Deploy & Post-Deploy Checklists
- Tier Sizing & Performance
- Troubleshooting
- Billing & Cost
- Safety Rules
- Reference Files
Files
SKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesreferences/connection-configs.mddoc13,152 bytesreferences/development-workflow.mddoc15,383 bytesreferences/mcp-troubleshooting.mddoc1,824 bytesreferences/output-diagnostics.mddoc6,749 bytesreferences/pipeline-patterns.mddoc16,447 bytesreferences/sizing-and-parallelism.mddoc6,267 bytes
Read SKILL.md
# MongoDB Atlas Streams
Build, operate, and debug Atlas Stream Processing (ASP) pipelines using four MCP tools from the MongoDB MCP Server.
## Prerequisites
This skill requires the **MongoDB MCP Server** connected with:
- Atlas API credentials (`apiClientId` and `apiClientSecret`)
The 4 tools: `atlas-streams-discover`, `atlas-streams-build`, `atlas-streams-manage`, `atlas-streams-teardown`.
**All operations require an Atlas project ID.** If unknown, call `atlas-list-projects` first to find your project ID.
## If MCP tools are unavailablemongodb-connection
When your agent uses it
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
What it loads into context
Frontmatter
146 tokensAlways available to the agent
SKILL.md
2,844 tokensWhen the skill is activated
Reference documents
1,784 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Principle: Context Before Configuration
- Understanding How Connection Pools Work
- Configuration Design
- Troubleshooting Connection Issues
- Environmental Context (MANDATORY)
- Advising on Monitoring & Iteration
- When creating code
Files
SKILL.mdcore13,926 bytesSKILL.mdcore13,926 bytesreferences/monitoring-guide.mddoc8,567 bytes
Read SKILL.md
# MongoDB Connection Optimizer
You are an expert in MongoDB connection management across all officially supported driver languages (Node.js, Python, Java, Go, C#, Ruby, PHP, etc.). Your role is to ensure connection configurations are optimized for the user's specific environment and requirements, avoiding the common pitfall of blindly applying arbitrary parameters.
## Core Principle: Context Before Configuration
**NEVER add connection pool parameters or timeout settings without first understanding the application's context.** Arbitrary values without justification lead to performance issues and harder-to-debug problems.
## Understanding How Connection Pools Work
- Connection pooling exists because establishing a MongoDB connection is expensive (TCP + TLS + auth = 50-500ms). Without pooling, every operation pays this cost.
- Open connections consume system memory on the MongoDB server instances, ~1 MB per connection on average, even when they are not active. It is advised to avoid having idle connections.
**Connection Lifecycle**: Borrow from pool → Execute operation → Return to pool → Prune idle connections exceeding `maxIdleTimeMS`.mongodb-mcp-setup
When your agent uses it
Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don't have the credentials set up.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
3,233 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Step 0: Detect Client
- Step 1: Check Existing Configuration
- Step 2: Present Configuration Options
- Step 3a: Connection String Setup
- Step 3b: Service Account Setup
- Step 3c: Atlas Local Setup
- Step 4: Determine Read-Only vs Read-Write Access
- Step 5: Configure Credentials
- Step 6: Next Steps
- Troubleshooting
Files
SKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytes
Read SKILL.md
# MongoDB MCP Server Setup
This skill guides users through configuring the MongoDB MCP server for use with an agentic client.
## Overview
The MongoDB MCP server requires authentication. Users have three options:
1. **Connection String** (Option A): Direct connection to a specific cluster
- Quick setup for single cluster
- Requires `MDB_MCP_CONNECTION_STRING` environment variable
2. **Service Account Credentials** (Option B): MongoDB Atlas Admin API access
- **Recommended for Atlas users** - simplifies authentication and data accessmongodb-natural-language-querying
When your agent uses it
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
What it loads into context
Frontmatter
195 tokensAlways available to the agent
SKILL.md
2,210 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Query Generation Process
- Best Practices
- Schema Analysis
- Sample Document Usage
- Error Handling
- Example Workflow
- Managing Context Size
Files
SKILL.mdcore9,630 bytes
Read SKILL.md
# MongoDB Natural Language Querying
You are an expert MongoDB read-only query and aggregation pipeline generator.
## Query Generation Process
### 1. Gather Context Using MCP Tools
**Required Information:**
- Database name and collection name (use `mcp__mongodb__list-databases` and `mcp__mongodb__list-collections` if not provided)
- User's natural language description of the query
**Fetch in this order:**
mongodb-qe-size-estimation
When your agent uses it
Estimates the storage and memory impact of encrypting fields in collections with Queryable Encryption (QE) enabled. Do NOT use for collections that use Client-Side Field Level Encryption (CSFLE) instead of QE.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
3,842 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- General Instructions
- 1. Prepare the Working Directory
- 2. State Purpose and Request Input Preference
- 3. Get the Number of Documents
- 4. Get the Number of Entries
- 5. Get Inputs Per Field
- 6. Add Formulas and Results to qe-sizing-calculations.md
- 7. Calculate Index Storage, Total Disk Storage, and Memory
- 8. Sum Total Results and Report Conclusions
- 9. Interpret Results
Files
SKILL.mdcore16,946 bytes
Read SKILL.md
# mongodb-qe-size-estimation
## General Instructions
- **CRITICAL: Never ask for or accept sample data.** QE is an encryption feature, meant to secure sensitive information. Do not request sample data, and if provided, reject it for security reasons. Inform the user that you can't accept sample documents, though you can take an encryption schema as an input to see which fields are encrypted, which allow queries, and what those query settings are.
- Any parenthetical in the form (LLM Note: <content>) is for LLM use. Don't save it to qe-sizing-calculations.md or mention it to the user.
- Tone: Be brief, not conversational. Don't restate information from prior exchanges unless a step specifically says to.
- Don't write anything to the file except what's specified in the steps.
- This skill relies on a question-answer-flow to get all required inputs. For Steps 2-5, as long as the user is providing valid input, your responses should include both acknowledgment of the input, and the next question.
- The user may try to keep things moving by inputting multiple inputs. If inputs don't clearly map to a field and its configuration, ask for clarification. Otherwise validate them, and accept them if valid.
- This skill currently has no formula for range queries, so while it accepts "range" query configuration for compatible BSON types, it uses 0 for estimated values.
### Definitionsmongodb-query-optimizer
When your agent uses it
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
What it loads into context
Frontmatter
182 tokensAlways available to the agent
SKILL.md
2,390 tokensWhen the skill is activated
Reference documents
3,766 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When this skill is invoked
- High Level Workflow
- MCP: available tools
- Example workflow 1 (help with specific query)
- Example workflow 2 (general database performance help)
- Load references
- Output
Files
SKILL.mdcore10,331 bytesreferences/aggregation-optimization.mddoc6,721 bytesreferences/antipattern-examples.mddoc2,527 bytesreferences/core-indexing-principles.mddoc4,319 bytesreferences/update-query-examples.mddoc1,144 bytes
Read SKILL.md
# MongoDB Query Optimizer
## When this skill is invoked
Invoke **only** when the user wants:
- Query/index **optimization** or **performance** help
- **Why** a query is slow or **how to speed it up**
- **Slow queries** on their cluster and/or **how to optimize them**
Do **not** invoke for routine query authoring unless the user has requested help with optimization, slow queries, or indexing.
## High Level Workflow
mongodb-schema-design
When your agent uses it
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
What it loads into context
Frontmatter
151 tokensAlways available to the agent
SKILL.md
2,823 tokensWhen the skill is activated
Reference documents
26,044 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Apply
- Quick Reference
- Key Principle
- Embed/Reference Decision Framework
- How to Use
- How These Rules Work
Files
SKILL.mdcore13,647 bytesSKILL.mdcore13,647 bytesSKILL.mdcore13,647 bytesreferences/antipattern-excessive-lookups.mddoc3,608 bytesreferences/antipattern-unnecessary-collections.mddoc4,194 bytesreferences/antipattern-unnecessary-indexes.mddoc3,773 bytesreferences/fundamental-document-model.mddoc3,608 bytesreferences/fundamental-document-size.mddoc9,803 bytesreferences/fundamental-embed-vs-reference.mddoc15,238 bytesreferences/fundamental-schema-validation.mddoc4,382 bytesreferences/pattern-approximation.mddoc3,310 bytesreferences/pattern-archive.mddoc5,456 bytesreferences/pattern-attribute.mddoc2,485 bytesreferences/pattern-bucket.mddoc4,268 bytesreferences/pattern-computed.mddoc6,163 bytesreferences/pattern-document-versioning.mddoc5,143 bytesreferences/pattern-extended-reference.mddoc3,403 bytesreferences/pattern-outlier.mddoc5,760 bytesreferences/pattern-polymorphic.mddoc6,115 bytesreferences/pattern-schema-versioning.mddoc8,419 bytesreferences/pattern-time-series-collections.mddoc6,611 bytesreferences/source-query-stats.mddoc3,431 bytesreferences/source-slow-query-logs.mddoc2,345 bytes
Read SKILL.md
# MongoDB Schema Design
Data modeling patterns and anti-patterns for MongoDB, maintained by MongoDB. Bad schema is the root cause of most MongoDB performance and cost issues—queries and indexes cannot fix a fundamentally wrong model.
## When to Apply
Reference these guidelines when:
- Designing a new MongoDB schema from scratch
- Migrating from SQL/relational databases to MongoDB
- Reviewing existing data models for performance issues
- Troubleshooting slow queries or growing document sizes
- Deciding between embedding and referencing
- Modeling relationships (one-to-one, one-to-many, many-to-many)
- Implementing tree/hierarchical structuresmongodb-search-and-ai
When your agent uses it
Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search (semantic), and Hybrid Search. Use this skill for ANY question about MongoDB search, including open-ended and explanatory ones asked before the user has any data or use case — 'how does search work in MongoDB?', 'I'm new to MongoDB search, where do I start?', 'what's the difference between Atlas Search and Vector Search?', 'which search type should I use?', 'what is vector search?', 'how does automated embedding work?', 'can you explain hybrid search?'. Do not answer these from general knowledge; use this skill. Also use to build search features: full-text queries, autocomplete, fuzzy matching, faceted search, semantic similarity, embeddings, RAG, text containment or substring matching ('contains', 'includes'), case-insensitive or multi-field text search, and filtering across many fields. Provides workflows for choosing a search type, creating indexes, writing queries, and optimizing performance via the MongoDB MCP server.
What it loads into context
Frontmatter
235 tokensAlways available to the agent
SKILL.md
1,890 tokensWhen the skill is activated
Reference documents
34,552 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Principles
- Workflow
- Anti-Patterns to Avoid
- Handling Edge Cases
Scripts executed separately
scripts/quickstart_complete.py
Files
SKILL.mdcore8,900 bytesreferences/automated-embedding.mddoc16,231 bytesreferences/hybrid-search.mddoc23,667 bytesreferences/lexical-search-indexing.mddoc19,995 bytesreferences/lexical-search-querying.mddoc17,878 bytesreferences/quick-start.mddoc46,196 bytesreferences/vector-search.mddoc19,133 bytesscripts/quickstart_complete.pyruns13,883 bytes
Read SKILL.md
# MongoDB Search and AI Recommendations Skill
You are helping MongoDB users implement, optimize, and troubleshoot Atlas Search (lexical), Vector Search (semantic), and Hybrid Search (combined) solutions.
## Core Principles
1. **Understand before building** - Validate the use case to ensure you recommend the right solution
2. **Always inspect first** - Check existing indexes and schema before making recommendations
3. **Explain before executing** - Describe what indexes will be created and require explicit approval
4. **Handle read-only scenarios** - Without `create`, `update`, or `delete` operation tools you are in read-only mode; hand the user index JSON to create themselves
5. **Explain in accessible language** - Describe technical concepts and map business requirements to technical implementations in terms the user can follow
## Workflow
mongodb-atlas-stream-processing
When your agent uses it
Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
4,681 tokensWhen the skill is activated
Reference documents
15,115 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites
- If MCP tools are unavailable
- Tool Selection Matrix
- CRITICAL: Validate Before Creating Processors
- Pipeline Rules & Warnings
- MCP Tool Behaviors
- Connection Capabilities — Source/Sink Reference
- Core Workflows
- Pre-Deploy & Post-Deploy Checklists
- Tier Sizing & Performance
- Troubleshooting
- Billing & Cost
- Safety Rules
- Reference Files
Files
SKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesSKILL.mdcore19,851 bytesreferences/connection-configs.mddoc13,152 bytesreferences/development-workflow.mddoc15,383 bytesreferences/mcp-troubleshooting.mddoc1,824 bytesreferences/output-diagnostics.mddoc6,749 bytesreferences/pipeline-patterns.mddoc16,447 bytesreferences/sizing-and-parallelism.mddoc6,267 bytes
Read SKILL.md
# MongoDB Atlas Streams
Build, operate, and debug Atlas Stream Processing (ASP) pipelines using four MCP tools from the MongoDB MCP Server.
## Prerequisites
This skill requires the **MongoDB MCP Server** connected with:
- Atlas API credentials (`apiClientId` and `apiClientSecret`)
The 4 tools: `atlas-streams-discover`, `atlas-streams-build`, `atlas-streams-manage`, `atlas-streams-teardown`.
**All operations require an Atlas project ID.** If unknown, call `atlas-list-projects` first to find your project ID.
## If MCP tools are unavailablemongodb-connection
When your agent uses it
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
What it loads into context
Frontmatter
146 tokensAlways available to the agent
SKILL.md
2,844 tokensWhen the skill is activated
Reference documents
1,784 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Principle: Context Before Configuration
- Understanding How Connection Pools Work
- Configuration Design
- Troubleshooting Connection Issues
- Environmental Context (MANDATORY)
- Advising on Monitoring & Iteration
- When creating code
Files
SKILL.mdcore13,926 bytesSKILL.mdcore13,926 bytesreferences/monitoring-guide.mddoc8,567 bytes
Read SKILL.md
# MongoDB Connection Optimizer
You are an expert in MongoDB connection management across all officially supported driver languages (Node.js, Python, Java, Go, C#, Ruby, PHP, etc.). Your role is to ensure connection configurations are optimized for the user's specific environment and requirements, avoiding the common pitfall of blindly applying arbitrary parameters.
## Core Principle: Context Before Configuration
**NEVER add connection pool parameters or timeout settings without first understanding the application's context.** Arbitrary values without justification lead to performance issues and harder-to-debug problems.
## Understanding How Connection Pools Work
- Connection pooling exists because establishing a MongoDB connection is expensive (TCP + TLS + auth = 50-500ms). Without pooling, every operation pays this cost.
- Open connections consume system memory on the MongoDB server instances, ~1 MB per connection on average, even when they are not active. It is advised to avoid having idle connections.
**Connection Lifecycle**: Borrow from pool → Execute operation → Return to pool → Prune idle connections exceeding `maxIdleTimeMS`.mongodb-mcp-setup
When your agent uses it
Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don't have the credentials set up.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
3,233 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Step 0: Detect Client
- Step 1: Check Existing Configuration
- Step 2: Present Configuration Options
- Step 3a: Connection String Setup
- Step 3b: Service Account Setup
- Step 3c: Atlas Local Setup
- Step 4: Determine Read-Only vs Read-Write Access
- Step 5: Configure Credentials
- Step 6: Next Steps
- Troubleshooting
Files
SKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytesSKILL.mdcore13,319 bytes
Read SKILL.md
# MongoDB MCP Server Setup
This skill guides users through configuring the MongoDB MCP server for use with an agentic client.
## Overview
The MongoDB MCP server requires authentication. Users have three options:
1. **Connection String** (Option A): Direct connection to a specific cluster
- Quick setup for single cluster
- Requires `MDB_MCP_CONNECTION_STRING` environment variable
2. **Service Account Credentials** (Option B): MongoDB Atlas Admin API access
- **Recommended for Atlas users** - simplifies authentication and data accessmongodb-natural-language-querying
When your agent uses it
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
What it loads into context
Frontmatter
195 tokensAlways available to the agent
SKILL.md
2,210 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Query Generation Process
- Best Practices
- Schema Analysis
- Sample Document Usage
- Error Handling
- Example Workflow
- Managing Context Size
Files
SKILL.mdcore9,630 bytes
Read SKILL.md
# MongoDB Natural Language Querying
You are an expert MongoDB read-only query and aggregation pipeline generator.
## Query Generation Process
### 1. Gather Context Using MCP Tools
**Required Information:**
- Database name and collection name (use `mcp__mongodb__list-databases` and `mcp__mongodb__list-collections` if not provided)
- User's natural language description of the query
**Fetch in this order:**
mongodb-qe-size-estimation
When your agent uses it
Estimates the storage and memory impact of encrypting fields in collections with Queryable Encryption (QE) enabled. Do NOT use for collections that use Client-Side Field Level Encryption (CSFLE) instead of QE.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
3,842 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- General Instructions
- 1. Prepare the Working Directory
- 2. State Purpose and Request Input Preference
- 3. Get the Number of Documents
- 4. Get the Number of Entries
- 5. Get Inputs Per Field
- 6. Add Formulas and Results to qe-sizing-calculations.md
- 7. Calculate Index Storage, Total Disk Storage, and Memory
- 8. Sum Total Results and Report Conclusions
- 9. Interpret Results
Files
SKILL.mdcore16,946 bytes
Read SKILL.md
# mongodb-qe-size-estimation
## General Instructions
- **CRITICAL: Never ask for or accept sample data.** QE is an encryption feature, meant to secure sensitive information. Do not request sample data, and if provided, reject it for security reasons. Inform the user that you can't accept sample documents, though you can take an encryption schema as an input to see which fields are encrypted, which allow queries, and what those query settings are.
- Any parenthetical in the form (LLM Note: <content>) is for LLM use. Don't save it to qe-sizing-calculations.md or mention it to the user.
- Tone: Be brief, not conversational. Don't restate information from prior exchanges unless a step specifically says to.
- Don't write anything to the file except what's specified in the steps.
- This skill relies on a question-answer-flow to get all required inputs. For Steps 2-5, as long as the user is providing valid input, your responses should include both acknowledgment of the input, and the next question.
- The user may try to keep things moving by inputting multiple inputs. If inputs don't clearly map to a field and its configuration, ask for clarification. Otherwise validate them, and accept them if valid.
- This skill currently has no formula for range queries, so while it accepts "range" query configuration for compatible BSON types, it uses 0 for estimated values.
### Definitionsmongodb-query-optimizer
When your agent uses it
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
What it loads into context
Frontmatter
182 tokensAlways available to the agent
SKILL.md
2,390 tokensWhen the skill is activated
Reference documents
3,766 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When this skill is invoked
- High Level Workflow
- MCP: available tools
- Example workflow 1 (help with specific query)
- Example workflow 2 (general database performance help)
- Load references
- Output
Files
SKILL.mdcore10,331 bytesreferences/aggregation-optimization.mddoc6,721 bytesreferences/antipattern-examples.mddoc2,527 bytesreferences/core-indexing-principles.mddoc4,319 bytesreferences/update-query-examples.mddoc1,144 bytes
Read SKILL.md
# MongoDB Query Optimizer
## When this skill is invoked
Invoke **only** when the user wants:
- Query/index **optimization** or **performance** help
- **Why** a query is slow or **how to speed it up**
- **Slow queries** on their cluster and/or **how to optimize them**
Do **not** invoke for routine query authoring unless the user has requested help with optimization, slow queries, or indexing.
## High Level Workflow
mongodb-schema-design
When your agent uses it
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
What it loads into context
Frontmatter
151 tokensAlways available to the agent
SKILL.md
2,823 tokensWhen the skill is activated
Reference documents
26,044 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Apply
- Quick Reference
- Key Principle
- Embed/Reference Decision Framework
- How to Use
- How These Rules Work
Files
SKILL.mdcore13,647 bytesSKILL.mdcore13,647 bytesSKILL.mdcore13,647 bytesreferences/antipattern-excessive-lookups.mddoc3,608 bytesreferences/antipattern-unnecessary-collections.mddoc4,194 bytesreferences/antipattern-unnecessary-indexes.mddoc3,773 bytesreferences/fundamental-document-model.mddoc3,608 bytesreferences/fundamental-document-size.mddoc9,803 bytesreferences/fundamental-embed-vs-reference.mddoc15,238 bytesreferences/fundamental-schema-validation.mddoc4,382 bytesreferences/pattern-approximation.mddoc3,310 bytesreferences/pattern-archive.mddoc5,456 bytesreferences/pattern-attribute.mddoc2,485 bytesreferences/pattern-bucket.mddoc4,268 bytesreferences/pattern-computed.mddoc6,163 bytesreferences/pattern-document-versioning.mddoc5,143 bytesreferences/pattern-extended-reference.mddoc3,403 bytesreferences/pattern-outlier.mddoc5,760 bytesreferences/pattern-polymorphic.mddoc6,115 bytesreferences/pattern-schema-versioning.mddoc8,419 bytesreferences/pattern-time-series-collections.mddoc6,611 bytesreferences/source-query-stats.mddoc3,431 bytesreferences/source-slow-query-logs.mddoc2,345 bytes
Read SKILL.md
# MongoDB Schema Design
Data modeling patterns and anti-patterns for MongoDB, maintained by MongoDB. Bad schema is the root cause of most MongoDB performance and cost issues—queries and indexes cannot fix a fundamentally wrong model.
## When to Apply
Reference these guidelines when:
- Designing a new MongoDB schema from scratch
- Migrating from SQL/relational databases to MongoDB
- Reviewing existing data models for performance issues
- Troubleshooting slow queries or growing document sizes
- Deciding between embedding and referencing
- Modeling relationships (one-to-one, one-to-many, many-to-many)
- Implementing tree/hierarchical structuresmongodb-search-and-ai
When your agent uses it
Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search (semantic), and Hybrid Search. Use this skill for ANY question about MongoDB search, including open-ended and explanatory ones asked before the user has any data or use case — 'how does search work in MongoDB?', 'I'm new to MongoDB search, where do I start?', 'what's the difference between Atlas Search and Vector Search?', 'which search type should I use?', 'what is vector search?', 'how does automated embedding work?', 'can you explain hybrid search?'. Do not answer these from general knowledge; use this skill. Also use to build search features: full-text queries, autocomplete, fuzzy matching, faceted search, semantic similarity, embeddings, RAG, text containment or substring matching ('contains', 'includes'), case-insensitive or multi-field text search, and filtering across many fields. Provides workflows for choosing a search type, creating indexes, writing queries, and optimizing performance via the MongoDB MCP server.
What it loads into context
Frontmatter
235 tokensAlways available to the agent
SKILL.md
1,890 tokensWhen the skill is activated
Reference documents
34,552 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Principles
- Workflow
- Anti-Patterns to Avoid
- Handling Edge Cases
Scripts executed separately
scripts/quickstart_complete.py
Files
SKILL.mdcore8,900 bytesreferences/automated-embedding.mddoc16,231 bytesreferences/hybrid-search.mddoc23,667 bytesreferences/lexical-search-indexing.mddoc19,995 bytesreferences/lexical-search-querying.mddoc17,878 bytesreferences/quick-start.mddoc46,196 bytesreferences/vector-search.mddoc19,133 bytesscripts/quickstart_complete.pyruns13,883 bytes
Read SKILL.md
# MongoDB Search and AI Recommendations Skill
You are helping MongoDB users implement, optimize, and troubleshoot Atlas Search (lexical), Vector Search (semantic), and Hybrid Search (combined) solutions.
## Core Principles
1. **Understand before building** - Validate the use case to ensure you recommend the right solution
2. **Always inspect first** - Check existing indexes and schema before making recommendations
3. **Explain before executing** - Describe what indexes will be created and require explicit approval
4. **Handle read-only scenarios** - Without `create`, `update`, or `delete` operation tools you are in read-only mode; hand the user index JSON to create themselves
5. **Explain in accessible language** - Describe technical concepts and map business requirements to technical implementations in terms the user can follow
## Workflow
review-skill
When your agent uses it
Review a proposed Agent Skill for structural validity and content quality before publishing. Runs the skill-validator CLI to check for structural issues, scores the skill with an LLM judge, and interprets results to advise SMEs on what to address. Use when a user wants to review, validate, or quality-check an Agent Skill.
What it loads into context
Frontmatter
123 tokensAlways available to the agent
SKILL.md
1,524 tokensWhen the skill is activated
Reference documents
2,819 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 0: Determine Environment
- Step 1: Verify Prerequisites
- Step 2: Locate the Skill
- Step 3: Run Structural Validation
- Step 4: Content Review
- Step 5: LLM Scoring and Interpretation
- Step 6: Present the Review Summary
- Example Review Summary Structure
Files
SKILL.mdcore6,220 bytesSKILL.mdcore6,220 bytesSKILL.mdcore6,220 bytesSKILL.mdcore6,220 bytesSKILL.mdcore6,220 bytesSKILL.mdcore6,220 bytesSKILL.mdcore6,220 bytesassets/report.mddoc6,929 bytesreferences/install-skill-validator.mddoc1,164 bytesreferences/llm-scoring.mddoc4,878 bytes
Read SKILL.md
# Review Skill Workflow
You are helping an SME review an Agent Skill before publishing. This is a
multi-step process: determine environment, verify prerequisites, run structural
validation, review content, optionally run LLM scoring, and interpret results.
Follow every step in order.
## Step 0: Determine Environment
Check for saved configuration:
```bash
cat ~/.config/skill-validator/review-state.yaml 2>/dev/null
```Skills included
- mongodb-atlas-stream-processing
- mongodb-connection
- mongodb-natural-language-querying
- mongodb-qe-size-estimation
- mongodb-query-optimizer
- mongodb-schema-design
- mongodb-search-and-ai
- mongodb-atlas-stream-processing
- mongodb-connection
- mongodb-mcp-setup
- mongodb-natural-language-querying
- mongodb-qe-size-estimation
- mongodb-query-optimizer
- mongodb-schema-design
- mongodb-search-and-ai
- mongodb-atlas-stream-processing
- mongodb-connection
- mongodb-mcp-setup
- mongodb-natural-language-querying
- mongodb-qe-size-estimation
- mongodb-query-optimizer
- mongodb-schema-design
- mongodb-search-and-ai
- review-skill
Repository facts
- License
- Apache-2.0
- Created
- Feb 26, 2026
- Last pushed
- Nov 3, 2026
- Forks
- 44
- Open issues
- 0
- Project activity
- Active
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 6, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 53 notes
plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb-atlas/skills/mongodb-connection/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb-atlas/skills/mongodb-schema-design/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb-atlas/skills/mongodb-schema-design/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-atlas-stream-processing/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-atlas-stream-processing/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-atlas-stream-processing/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-atlas-stream-processing/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-connection/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- 3 dangerous shell commandsin 3 files: review-skill/SKILL.md:79 review-skill/references/install-skill-validator.md:43 review-skill/references/llm-scoring.md:48
- 337 network referencesin 89 files: plugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-embed-vs-reference.md:51 plugins/mongodb/skills/mongodb-atlas-stream-processing/references/connection-configs.md:3 plugins/mongodb/skills/mongodb-schema-design/references/fundamental-embed-vs-reference.md:51 and 86 more
- No hidden or obfuscated text
- No instruction overrides
- 28 references to secretsin 5 files: plugins/mongodb/skills/mongodb-mcp-setup/SKILL.md:25 mongodb-mcp-setup/SKILL.md:25 plugins/mongodb-atlas/skills/mongodb-search-and-ai/scripts/quickstart_complete.py:41 and 2 more
- No requests to vote in Hot
Show all 373 signals by file
plugins/mongodb-atlas/.claude-plugin/plugin.json
- Network accessline 1
{ "$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json", - Network accessline 2
"$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json", - Network accessline 8
"name": "MongoDB", "email": "agent-plugins@mongodb.com", "url": "https://www.mongodb.com" - Network accessline 9
"email": "agent-plugins@mongodb.com", "url": "https://www.mongodb.com" }, - Network accessline 10
"url": "https://www.mongodb.com" - Network accessline 11
}, "homepage": "https://www.mongodb.com/docs/mcp-server/get-started/", "repository": "https://github.com/mongodb/agent-skills", - Network accessline 12
"homepage": "https://www.mongodb.com/docs/mcp-server/get-started/", - Network accessline 13
"repository": "https://github.com/mongodb/agent-skills", - Network accessline 24
"mongodb-atlas": { "type": "http", "url": "https://mcp.mongodb.com/", - Network accessline 25
"type": "http", "url": "https://mcp.mongodb.com/", "oauth": { - Network accessline 26
"url": "https://mcp.mongodb.com/", - Network accessline 27
"oauth": { "clientId": "https://claude.ai/oauth/claude-code-client-metadata" } - Network accessline 28
"clientId": "https://claude.ai/oauth/claude-code-client-metadata"
plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md
- Network accessline 116
Also fetch examples from the official ASP examples repo when building non-trivial processors: **https://github.com/mongodb/ASP_example** (quickstarts, example processors, Terraform examples). Start with `example_processors/README.md` for th - Network accessline 204
3. `atlas-streams-build` → `resource: "connection"` (for each source/sink/enrichment) 4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary t - Network accessline 205
4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary to user 5. Call `search-knowledge` to validate field names. Fetch relevant examples fro - Network accessline 206
5. Call `search-knowledge` to validate field names. Fetch relevant examples from https://github.com/mongodb/ASP_example
plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/references/connection-configs.md
- Network accessline 3
**Official examples repo**: https://github.com/mongodb/ASP_example — check quickstarts, example processors, and Terraform examples. Start with quickstarts. - Network accessline 189
{ "url": "https://api.example.com/webhook", - Network accessline 190
"url": "https://api.example.com/webhook", - Network accessline 204
[Credential-related source omitted] - Network accessline 209
[Credential-related source omitted] - Network accessline 214
[Credential-related source omitted] - Network accessline 219
[Credential-related source omitted] - Network accessline 247
"connectionType": "SchemaRegistry", "connectionConfig": { "schemaRegistryUrls": ["https://schema-registry.example.com"], - Network accessline 248
"connectionConfig": { "schemaRegistryUrls": ["https://schema-registry.example.com"], "schemaRegistryAuthentication": { - Network accessline 249
"schemaRegistryUrls": ["https://schema-registry.example.com"],
plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/references/development-workflow.md
- Network accessline 98
- ⚠<U+FE0F> atlascluster → ClusterRestoreTest (READY) Warning: Connection name 'atlascluster' doesn't match actual cluster 'ClusterRestoreTest' - ✅ open-meteo-api → https://api.open-meteo.com/v1/... (READY) - Network accessline 100
- ✅ open-meteo-api → https://api.open-meteo.com/v1/... (READY)
plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/references/pipeline-patterns.md
- Network accessline 3
**Official examples repo**: https://github.com/mongodb/ASP_example (quickstarts, example processors, Terraform examples). Start with example_processors/README.md for the full pattern catalog.
plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/references/sizing-and-parallelism.md
- Network accessline 139
For current pricing: https://www.mongodb.com/docs/atlas/billing/stream-processing-costs/
plugins/mongodb-atlas/skills/mongodb-connection/references/monitoring-guide.md
- Network accessline 5
## Driver Events All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which - Network accessline 5
## Driver Events All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which - Network accessline 6
All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which defines standard - Network accessline 6
All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which defines standard - Network accessline 21
Access methods vary by driver. Consult your driver's [documentation](https://www.mongodb.com/docs/drivers/) for how to subscribe to these standard events. - Network accessline 21
Access methods vary by driver. Consult your driver's [documentation](https://www.mongodb.com/docs/drivers/) for how to subscribe to these standard events. - Network accessline 138
**See manual**: [db.serverStatus() documentation](https://www.mongodb.com/docs/manual/reference/command/serverStatus/#connections) - Network accessline 138
**See manual**: [db.serverStatus() documentation](https://www.mongodb.com/docs/manual/reference/command/serverStatus/#connections)
plugins/mongodb-atlas/skills/mongodb-query-optimizer/SKILL.md
- Network accessline 46
**How to invoke.** Call the **MongoDB MCP server** with the **exact tool name** as `toolName` and a single **arguments object** as `arguments`. Do not pass the tool name as an option, query param, or nested key; pass it as the MCP tool name - Network accessline 46
**How to invoke.** Call the **MongoDB MCP server** with the **exact tool name** as `toolName` and a single **arguments object** as `arguments`. Do not pass the tool name as an option, query param, or nested key; pass it as the MCP tool name
plugins/mongodb-atlas/skills/mongodb-schema-design/SKILL.md
plugins/mongodb-atlas/skills/mongodb-schema-design/references/antipattern-excessive-lookups.md
- Network accessline 84
Reference: [Reduce Lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/) - Network accessline 84
Reference: [Reduce Lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/antipattern-unnecessary-collections.md
- Network accessline 91
Reference: [Reduce the Number of Collections](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-collections/) - Network accessline 91
Reference: [Reduce the Number of Collections](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-collections/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/antipattern-unnecessary-indexes.md
plugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-document-model.md
plugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-document-size.md
- Network accessline 223
References: - [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 223
References: - [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 224
- [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 224
- [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 225
- [Avoid Unbounded Arrays](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/unbounded-arrays/) - Network accessline 225
- [Avoid Unbounded Arrays](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/unbounded-arrays/) - Network accessline 226
- [Reduce Bloated Documents](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/bloated-documents/) - Network accessline 226
- [Reduce Bloated Documents](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/bloated-documents/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-embed-vs-reference.md
- Network accessline 51
profile: { name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", - Network accessline 51
profile: { name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", - Network accessline 52
name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", bio: "Developer building cool things" - Network accessline 52
name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", bio: "Developer building cool things" - Network accessline 53
avatar: "https://cdn.example.com/alice.jpg", - Network accessline 53
avatar: "https://cdn.example.com/alice.jpg", - Network accessline 412
References: - [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 412
References: - [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 413
- [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 413
- [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 414
- [Model One-to-One Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-one-relationships-between-documents/) - Network accessline 414
- [Model One-to-One Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-one-relationships-between-documents/) - Network accessline 415
- [Model One-to-Many Relationships with Embedded Documents](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-many-relationships-between-documents/) - Network accessline 415
- [Model One-to-Many Relationships with Embedded Documents](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-many-relationships-between-documents/) - Network accessline 416
- [Model One-to-Many Relationships with References](https://mongodb.com/docs/manual/tutorial/model-referenced-one-to-many-relationships-between-documents/) - Network accessline 416
- [Model One-to-Many Relationships with References](https://mongodb.com/docs/manual/tutorial/model-referenced-one-to-many-relationships-between-documents/) - Network accessline 417
- [Model Many-to-Many Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-many-to-many-relationships-between-documents/) - Network accessline 417
- [Model Many-to-Many Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-many-to-many-relationships-between-documents/) - Network accessline 418
- [Model Tree Structures](https://mongodb.com/docs/manual/applications/data-models-tree-structures/) - Network accessline 418
- [Model Tree Structures](https://mongodb.com/docs/manual/applications/data-models-tree-structures/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-schema-validation.md
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-approximation.md
- Network accessline 80
Reference: [Use the Approximation Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/approximation-schema-pattern/) - Network accessline 80
Reference: [Use the Approximation Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/approximation-schema-pattern/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-archive.md
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-attribute.md
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-bucket.md
- Network accessline 12
> **For time-series data**, prefer [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/), which apply bucketing automatically with built-in compression and indexing optimizations. - Network accessline 12
> **For time-series data**, prefer [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/), which apply bucketing automatically with built-in compression and indexing optimizations. - Network accessline 75
- **Time-series workloads**: Use [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/) instead — they handle bucketing, compression, and indexing automatically. - Network accessline 75
- **Time-series workloads**: Use [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/) instead — they handle bucketing, compression, and indexing automatically. - Network accessline 102
Reference: [Group Data with the Bucket Pattern](https://www.mongodb.com/docs/manual/data-modeling/design-patterns/group-data/bucket-pattern/) - Network accessline 102
Reference: [Group Data with the Bucket Pattern](https://www.mongodb.com/docs/manual/data-modeling/design-patterns/group-data/bucket-pattern/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-computed.md
- Network accessline 133
When the computed results are best stored in a separate collection rather than embedded in the source documents, MongoDB's [on-demand materialized views](https://www.mongodb.com/docs/manual/core/materialized-views/) formalize this approach. - Network accessline 133
When the computed results are best stored in a separate collection rather than embedded in the source documents, MongoDB's [on-demand materialized views](https://www.mongodb.com/docs/manual/core/materialized-views/) formalize this approach. - Network accessline 162
Reference: [Computed Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/computed-schema-pattern/) - Network accessline 162
Reference: [Computed Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/computed-schema-pattern/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-document-versioning.md
- Network accessline 92
The `updatePolicy` function writes to two collections (inserting a revision **and** updating the current document). It may or may not be prudent to wrap the call in a [multi-document transaction](https://mongodb.com/docs/manual/core/transac - Network accessline 92
The `updatePolicy` function writes to two collections (inserting a revision **and** updating the current document). It may or may not be prudent to wrap the call in a [multi-document transaction](https://mongodb.com/docs/manual/core/transac - Network accessline 166
Reference: [Keep a History of Document Versions](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/document-versioning/) - Network accessline 166
Reference: [Keep a History of Document Versions](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/document-versioning/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-extended-reference.md
- Network accessline 72
Reference: [Reduce $lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/) - Network accessline 72
Reference: [Reduce $lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-outlier.md
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-polymorphic.md
- Network accessline 167
Reference: [Polymorphic Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/polymorphic-data/polymorphic-schema-pattern/) - Network accessline 167
Reference: [Polymorphic Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/polymorphic-data/polymorphic-schema-pattern/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-schema-versioning.md
- Network accessline 271
References: - [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 271
References: - [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 272
- [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 272
- [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 273
- [Schema Validation](https://mongodb.com/docs/manual/core/schema-validation/) - Network accessline 273
- [Schema Validation](https://mongodb.com/docs/manual/core/schema-validation/)
plugins/mongodb-atlas/skills/mongodb-schema-design/references/pattern-time-series-collections.md
plugins/mongodb-atlas/skills/mongodb-schema-design/references/source-slow-query-logs.md
- Network accessline 13
If the API call returns auth or access errors, see the [Performance Advisor docs](https://www.mongodb.com/docs/atlas/performance-advisor/). - Network accessline 13
If the API call returns auth or access errors, see the [Performance Advisor docs](https://www.mongodb.com/docs/atlas/performance-advisor/). - Network accessline 17
Atlas Admin API endpoint ([query parameters reference](https://www.mongodb.com/docs/ops-manager/current/reference/api/performance-advisor/get-slow-queries/#request-query-parameters)): - Network accessline 17
Atlas Admin API endpoint ([query parameters reference](https://www.mongodb.com/docs/ops-manager/current/reference/api/performance-advisor/get-slow-queries/#request-query-parameters)):
plugins/mongodb-atlas/skills/mongodb-search-and-ai/scripts/quickstart_complete.py
- Executable scriptline 1
#!/usr/bin/env python3 - Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted]
plugins/mongodb/.claude-plugin/plugin.json
- Network accessline 1
{ "$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json", - Network accessline 2
"$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json", - Network accessline 8
"name": "MongoDB", "email": "agent-plugins@mongodb.com", "url": "https://www.mongodb.com" - Network accessline 9
"email": "agent-plugins@mongodb.com", "url": "https://www.mongodb.com" }, - Network accessline 10
"url": "https://www.mongodb.com" - Network accessline 11
}, "homepage": "https://www.mongodb.com/docs/mcp-server/get-started/", "repository": "https://github.com/mongodb/agent-skills", - Network accessline 12
"homepage": "https://www.mongodb.com/docs/mcp-server/get-started/", - Network accessline 13
"repository": "https://github.com/mongodb/agent-skills",
plugins/mongodb/skills/mongodb-atlas-stream-processing/SKILL.md
- Network accessline 116
Also fetch examples from the official ASP examples repo when building non-trivial processors: **https://github.com/mongodb/ASP_example** (quickstarts, example processors, Terraform examples). Start with `example_processors/README.md` for th - Network accessline 116
Also fetch examples from the official ASP examples repo when building non-trivial processors: **https://github.com/mongodb/ASP_example** (quickstarts, example processors, Terraform examples). Start with `example_processors/README.md` for th - Network accessline 204
3. `atlas-streams-build` → `resource: "connection"` (for each source/sink/enrichment) 4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary t - Network accessline 204
3. `atlas-streams-build` → `resource: "connection"` (for each source/sink/enrichment) 4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary t - Network accessline 205
4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary to user 5. Call `search-knowledge` to validate field names. Fetch relevant examples fro - Network accessline 205
4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary to user 5. Call `search-knowledge` to validate field names. Fetch relevant examples fro - Network accessline 206
5. Call `search-knowledge` to validate field names. Fetch relevant examples from https://github.com/mongodb/ASP_example - Network accessline 206
5. Call `search-knowledge` to validate field names. Fetch relevant examples from https://github.com/mongodb/ASP_example
plugins/mongodb/skills/mongodb-atlas-stream-processing/references/connection-configs.md
- Network accessline 3
**Official examples repo**: https://github.com/mongodb/ASP_example — check quickstarts, example processors, and Terraform examples. Start with quickstarts. - Network accessline 3
**Official examples repo**: https://github.com/mongodb/ASP_example — check quickstarts, example processors, and Terraform examples. Start with quickstarts. - Network accessline 189
{ "url": "https://api.example.com/webhook", - Network accessline 189
{ "url": "https://api.example.com/webhook", - Network accessline 190
"url": "https://api.example.com/webhook", - Network accessline 190
"url": "https://api.example.com/webhook", - Network accessline 204
[Credential-related source omitted] - Network accessline 204
[Credential-related source omitted] - Network accessline 209
[Credential-related source omitted] - Network accessline 209
[Credential-related source omitted] - Network accessline 214
[Credential-related source omitted] - Network accessline 214
[Credential-related source omitted] - Network accessline 219
[Credential-related source omitted] - Network accessline 219
[Credential-related source omitted] - Network accessline 247
"connectionType": "SchemaRegistry", "connectionConfig": { "schemaRegistryUrls": ["https://schema-registry.example.com"], - Network accessline 247
"connectionType": "SchemaRegistry", "connectionConfig": { "schemaRegistryUrls": ["https://schema-registry.example.com"], - Network accessline 248
"connectionConfig": { "schemaRegistryUrls": ["https://schema-registry.example.com"], "schemaRegistryAuthentication": { - Network accessline 248
"connectionConfig": { "schemaRegistryUrls": ["https://schema-registry.example.com"], "schemaRegistryAuthentication": { - Network accessline 249
"schemaRegistryUrls": ["https://schema-registry.example.com"], - Network accessline 249
"schemaRegistryUrls": ["https://schema-registry.example.com"],
plugins/mongodb/skills/mongodb-atlas-stream-processing/references/development-workflow.md
- Network accessline 98
- ⚠<U+FE0F> atlascluster → ClusterRestoreTest (READY) Warning: Connection name 'atlascluster' doesn't match actual cluster 'ClusterRestoreTest' - ✅ open-meteo-api → https://api.open-meteo.com/v1/... (READY) - Network accessline 98
- ⚠<U+FE0F> atlascluster → ClusterRestoreTest (READY) Warning: Connection name 'atlascluster' doesn't match actual cluster 'ClusterRestoreTest' - ✅ open-meteo-api → https://api.open-meteo.com/v1/... (READY) - Network accessline 100
- ✅ open-meteo-api → https://api.open-meteo.com/v1/... (READY) - Network accessline 100
- ✅ open-meteo-api → https://api.open-meteo.com/v1/... (READY)
plugins/mongodb/skills/mongodb-atlas-stream-processing/references/pipeline-patterns.md
- Network accessline 3
**Official examples repo**: https://github.com/mongodb/ASP_example (quickstarts, example processors, Terraform examples). Start with example_processors/README.md for the full pattern catalog. - Network accessline 3
**Official examples repo**: https://github.com/mongodb/ASP_example (quickstarts, example processors, Terraform examples). Start with example_processors/README.md for the full pattern catalog.
plugins/mongodb/skills/mongodb-atlas-stream-processing/references/sizing-and-parallelism.md
plugins/mongodb/skills/mongodb-connection/references/monitoring-guide.md
- Network accessline 5
## Driver Events All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which - Network accessline 5
## Driver Events All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which - Network accessline 6
All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which defines standard - Network accessline 6
All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which defines standard - Network accessline 21
Access methods vary by driver. Consult your driver's [documentation](https://www.mongodb.com/docs/drivers/) for how to subscribe to these standard events. - Network accessline 21
Access methods vary by driver. Consult your driver's [documentation](https://www.mongodb.com/docs/drivers/) for how to subscribe to these standard events. - Network accessline 138
**See manual**: [db.serverStatus() documentation](https://www.mongodb.com/docs/manual/reference/command/serverStatus/#connections) - Network accessline 138
**See manual**: [db.serverStatus() documentation](https://www.mongodb.com/docs/manual/reference/command/serverStatus/#connections)
plugins/mongodb/skills/mongodb-mcp-setup/SKILL.md
- Access to secretsline 25
[Credential-related source omitted] - Access to secretsline 25
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Network accessline 112
1. Go to [cloud.mongodb.com](https://cloud.mongodb.com) - Network accessline 112
1. Go to [cloud.mongodb.com](https://cloud.mongodb.com) - Network accessline 138
**Full documentation**: https://www.mongodb.com/docs/mcp-server/prerequisites/ - Network accessline 138
**Full documentation**: https://www.mongodb.com/docs/mcp-server/prerequisites/ - Network accessline 142
1. **Navigate to MongoDB Atlas** — [cloud.mongodb.com](https://cloud.mongodb.com) - Network accessline 142
1. **Navigate to MongoDB Atlas** — [cloud.mongodb.com](https://cloud.mongodb.com) - Network accessline 178
If not installed, direct them to: https://www.docker.com/get-started - Network accessline 178
If not installed, direct them to: https://www.docker.com/get-started - Access to secretsline 240
[Credential-related source omitted] - Access to secretsline 240
[Credential-related source omitted] - Access to secretsline 248
[Credential-related source omitted] - Access to secretsline 248
[Credential-related source omitted] - Access to secretsline 285
[Credential-related source omitted] - Access to secretsline 285
[Credential-related source omitted]
plugins/mongodb/skills/mongodb-query-optimizer/SKILL.md
- Network accessline 46
**How to invoke.** Call the **MongoDB MCP server** with the **exact tool name** as `toolName` and a single **arguments object** as `arguments`. Do not pass the tool name as an option, query param, or nested key; pass it as the MCP tool name - Network accessline 46
**How to invoke.** Call the **MongoDB MCP server** with the **exact tool name** as `toolName` and a single **arguments object** as `arguments`. Do not pass the tool name as an option, query param, or nested key; pass it as the MCP tool name
plugins/mongodb/skills/mongodb-schema-design/SKILL.md
plugins/mongodb/skills/mongodb-schema-design/references/antipattern-excessive-lookups.md
- Network accessline 84
Reference: [Reduce Lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/) - Network accessline 84
Reference: [Reduce Lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/)
plugins/mongodb/skills/mongodb-schema-design/references/antipattern-unnecessary-collections.md
- Network accessline 91
Reference: [Reduce the Number of Collections](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-collections/) - Network accessline 91
Reference: [Reduce the Number of Collections](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-collections/)
plugins/mongodb/skills/mongodb-schema-design/references/antipattern-unnecessary-indexes.md
plugins/mongodb/skills/mongodb-schema-design/references/fundamental-document-model.md
plugins/mongodb/skills/mongodb-schema-design/references/fundamental-document-size.md
- Network accessline 223
References: - [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 223
References: - [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 224
- [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 224
- [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 225
- [Avoid Unbounded Arrays](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/unbounded-arrays/) - Network accessline 225
- [Avoid Unbounded Arrays](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/unbounded-arrays/) - Network accessline 226
- [Reduce Bloated Documents](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/bloated-documents/) - Network accessline 226
- [Reduce Bloated Documents](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/bloated-documents/)
plugins/mongodb/skills/mongodb-schema-design/references/fundamental-embed-vs-reference.md
- Network accessline 51
profile: { name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", - Network accessline 51
profile: { name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", - Network accessline 52
name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", bio: "Developer building cool things" - Network accessline 52
name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", bio: "Developer building cool things" - Network accessline 53
avatar: "https://cdn.example.com/alice.jpg", - Network accessline 53
avatar: "https://cdn.example.com/alice.jpg", - Network accessline 412
References: - [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 412
References: - [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 413
- [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 413
- [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 414
- [Model One-to-One Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-one-relationships-between-documents/) - Network accessline 414
- [Model One-to-One Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-one-relationships-between-documents/) - Network accessline 415
- [Model One-to-Many Relationships with Embedded Documents](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-many-relationships-between-documents/) - Network accessline 415
- [Model One-to-Many Relationships with Embedded Documents](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-many-relationships-between-documents/) - Network accessline 416
- [Model One-to-Many Relationships with References](https://mongodb.com/docs/manual/tutorial/model-referenced-one-to-many-relationships-between-documents/) - Network accessline 416
- [Model One-to-Many Relationships with References](https://mongodb.com/docs/manual/tutorial/model-referenced-one-to-many-relationships-between-documents/) - Network accessline 417
- [Model Many-to-Many Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-many-to-many-relationships-between-documents/) - Network accessline 417
- [Model Many-to-Many Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-many-to-many-relationships-between-documents/) - Network accessline 418
- [Model Tree Structures](https://mongodb.com/docs/manual/applications/data-models-tree-structures/) - Network accessline 418
- [Model Tree Structures](https://mongodb.com/docs/manual/applications/data-models-tree-structures/)
plugins/mongodb/skills/mongodb-schema-design/references/fundamental-schema-validation.md
plugins/mongodb/skills/mongodb-schema-design/references/pattern-approximation.md
- Network accessline 80
Reference: [Use the Approximation Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/approximation-schema-pattern/) - Network accessline 80
Reference: [Use the Approximation Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/approximation-schema-pattern/)
plugins/mongodb/skills/mongodb-schema-design/references/pattern-archive.md
plugins/mongodb/skills/mongodb-schema-design/references/pattern-attribute.md
plugins/mongodb/skills/mongodb-schema-design/references/pattern-bucket.md
- Network accessline 12
> **For time-series data**, prefer [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/), which apply bucketing automatically with built-in compression and indexing optimizations. - Network accessline 12
> **For time-series data**, prefer [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/), which apply bucketing automatically with built-in compression and indexing optimizations. - Network accessline 75
- **Time-series workloads**: Use [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/) instead — they handle bucketing, compression, and indexing automatically. - Network accessline 75
- **Time-series workloads**: Use [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/) instead — they handle bucketing, compression, and indexing automatically. - Network accessline 102
Reference: [Group Data with the Bucket Pattern](https://www.mongodb.com/docs/manual/data-modeling/design-patterns/group-data/bucket-pattern/) - Network accessline 102
Reference: [Group Data with the Bucket Pattern](https://www.mongodb.com/docs/manual/data-modeling/design-patterns/group-data/bucket-pattern/)
plugins/mongodb/skills/mongodb-schema-design/references/pattern-computed.md
- Network accessline 133
When the computed results are best stored in a separate collection rather than embedded in the source documents, MongoDB's [on-demand materialized views](https://www.mongodb.com/docs/manual/core/materialized-views/) formalize this approach. - Network accessline 133
When the computed results are best stored in a separate collection rather than embedded in the source documents, MongoDB's [on-demand materialized views](https://www.mongodb.com/docs/manual/core/materialized-views/) formalize this approach. - Network accessline 162
Reference: [Computed Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/computed-schema-pattern/) - Network accessline 162
Reference: [Computed Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/computed-schema-pattern/)
plugins/mongodb/skills/mongodb-schema-design/references/pattern-document-versioning.md
- Network accessline 92
The `updatePolicy` function writes to two collections (inserting a revision **and** updating the current document). It may or may not be prudent to wrap the call in a [multi-document transaction](https://mongodb.com/docs/manual/core/transac - Network accessline 92
The `updatePolicy` function writes to two collections (inserting a revision **and** updating the current document). It may or may not be prudent to wrap the call in a [multi-document transaction](https://mongodb.com/docs/manual/core/transac - Network accessline 166
Reference: [Keep a History of Document Versions](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/document-versioning/) - Network accessline 166
Reference: [Keep a History of Document Versions](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/document-versioning/)
plugins/mongodb/skills/mongodb-schema-design/references/pattern-extended-reference.md
- Network accessline 72
Reference: [Reduce $lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/) - Network accessline 72
Reference: [Reduce $lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/)
plugins/mongodb/skills/mongodb-schema-design/references/pattern-outlier.md
plugins/mongodb/skills/mongodb-schema-design/references/pattern-polymorphic.md
- Network accessline 167
Reference: [Polymorphic Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/polymorphic-data/polymorphic-schema-pattern/) - Network accessline 167
Reference: [Polymorphic Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/polymorphic-data/polymorphic-schema-pattern/)
plugins/mongodb/skills/mongodb-schema-design/references/pattern-schema-versioning.md
- Network accessline 271
References: - [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 271
References: - [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 272
- [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 272
- [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 273
- [Schema Validation](https://mongodb.com/docs/manual/core/schema-validation/) - Network accessline 273
- [Schema Validation](https://mongodb.com/docs/manual/core/schema-validation/)
plugins/mongodb/skills/mongodb-schema-design/references/pattern-time-series-collections.md
plugins/mongodb/skills/mongodb-schema-design/references/source-slow-query-logs.md
- Network accessline 13
If the API call returns auth or access errors, see the [Performance Advisor docs](https://www.mongodb.com/docs/atlas/performance-advisor/). - Network accessline 13
If the API call returns auth or access errors, see the [Performance Advisor docs](https://www.mongodb.com/docs/atlas/performance-advisor/). - Network accessline 17
Atlas Admin API endpoint ([query parameters reference](https://www.mongodb.com/docs/ops-manager/current/reference/api/performance-advisor/get-slow-queries/#request-query-parameters)): - Network accessline 17
Atlas Admin API endpoint ([query parameters reference](https://www.mongodb.com/docs/ops-manager/current/reference/api/performance-advisor/get-slow-queries/#request-query-parameters)):
plugins/mongodb/skills/mongodb-search-and-ai/scripts/quickstart_complete.py
- Executable scriptline 1
#!/usr/bin/env python3 - Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted]
skills/mongodb-atlas-stream-processing/SKILL.md
- Network accessline 116
Also fetch examples from the official ASP examples repo when building non-trivial processors: **https://github.com/mongodb/ASP_example** (quickstarts, example processors, Terraform examples). Start with `example_processors/README.md` for th - Network accessline 204
3. `atlas-streams-build` → `resource: "connection"` (for each source/sink/enrichment) 4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary t - Network accessline 205
4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary to user 5. Call `search-knowledge` to validate field names. Fetch relevant examples fro - Network accessline 206
5. Call `search-knowledge` to validate field names. Fetch relevant examples from https://github.com/mongodb/ASP_example
skills/mongodb-atlas-stream-processing/references/connection-configs.md
- Network accessline 3
**Official examples repo**: https://github.com/mongodb/ASP_example — check quickstarts, example processors, and Terraform examples. Start with quickstarts. - Network accessline 189
{ "url": "https://api.example.com/webhook", - Network accessline 190
"url": "https://api.example.com/webhook", - Network accessline 204
[Credential-related source omitted] - Network accessline 209
[Credential-related source omitted] - Network accessline 214
[Credential-related source omitted] - Network accessline 219
[Credential-related source omitted] - Network accessline 247
"connectionType": "SchemaRegistry", "connectionConfig": { "schemaRegistryUrls": ["https://schema-registry.example.com"], - Network accessline 248
"connectionConfig": { "schemaRegistryUrls": ["https://schema-registry.example.com"], "schemaRegistryAuthentication": { - Network accessline 249
"schemaRegistryUrls": ["https://schema-registry.example.com"],
skills/mongodb-atlas-stream-processing/references/development-workflow.md
- Network accessline 98
- ⚠<U+FE0F> atlascluster → ClusterRestoreTest (READY) Warning: Connection name 'atlascluster' doesn't match actual cluster 'ClusterRestoreTest' - ✅ open-meteo-api → https://api.open-meteo.com/v1/... (READY) - Network accessline 100
- ✅ open-meteo-api → https://api.open-meteo.com/v1/... (READY)
skills/mongodb-atlas-stream-processing/references/pipeline-patterns.md
- Network accessline 3
**Official examples repo**: https://github.com/mongodb/ASP_example (quickstarts, example processors, Terraform examples). Start with example_processors/README.md for the full pattern catalog.
skills/mongodb-atlas-stream-processing/references/sizing-and-parallelism.md
- Network accessline 139
For current pricing: https://www.mongodb.com/docs/atlas/billing/stream-processing-costs/
skills/mongodb-connection/references/monitoring-guide.md
- Network accessline 5
## Driver Events All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which - Network accessline 6
All MongoDB drivers implement the [Connection Monitoring and Pooling specification](https://github.com/mongodb/specifications/blob/master/source/connection-monitoring-and-pooling/connection-monitoring-and-pooling.md), which defines standard - Network accessline 21
Access methods vary by driver. Consult your driver's [documentation](https://www.mongodb.com/docs/drivers/) for how to subscribe to these standard events. - Network accessline 138
**See manual**: [db.serverStatus() documentation](https://www.mongodb.com/docs/manual/reference/command/serverStatus/#connections)
skills/mongodb-mcp-setup/SKILL.md
- Access to secretsline 25
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Network accessline 112
1. Go to [cloud.mongodb.com](https://cloud.mongodb.com) - Network accessline 138
**Full documentation**: https://www.mongodb.com/docs/mcp-server/prerequisites/ - Network accessline 142
1. **Navigate to MongoDB Atlas** — [cloud.mongodb.com](https://cloud.mongodb.com) - Network accessline 178
If not installed, direct them to: https://www.docker.com/get-started - Access to secretsline 240
[Credential-related source omitted] - Access to secretsline 248
[Credential-related source omitted] - Access to secretsline 285
[Credential-related source omitted]
skills/mongodb-query-optimizer/SKILL.md
- Network accessline 46
**How to invoke.** Call the **MongoDB MCP server** with the **exact tool name** as `toolName` and a single **arguments object** as `arguments`. Do not pass the tool name as an option, query param, or nested key; pass it as the MCP tool name
skills/mongodb-schema-design/SKILL.md
- Network accessline 141
For automatic verification, connect the [MongoDB MCP Server](https://github.com/mongodb-js/mongodb-mcp-server).
skills/mongodb-schema-design/references/antipattern-excessive-lookups.md
- Network accessline 84
Reference: [Reduce Lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/)
skills/mongodb-schema-design/references/antipattern-unnecessary-collections.md
- Network accessline 91
Reference: [Reduce the Number of Collections](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-collections/)
skills/mongodb-schema-design/references/antipattern-unnecessary-indexes.md
- Network accessline 95
Reference: [Remove Unnecessary Indexes](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/unnecessary-indexes/)
skills/mongodb-schema-design/references/fundamental-document-model.md
- Network accessline 91
Reference: [Schema Design Process](https://mongodb.com/docs/manual/data-modeling/schema-design-process/)
skills/mongodb-schema-design/references/fundamental-document-size.md
- Network accessline 223
References: - [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 224
- [BSON Document Size Limit](https://mongodb.com/docs/manual/reference/limits/#std-label-limit-bson-document-size) - Network accessline 225
- [Avoid Unbounded Arrays](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/unbounded-arrays/) - Network accessline 226
- [Reduce Bloated Documents](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/bloated-documents/)
skills/mongodb-schema-design/references/fundamental-embed-vs-reference.md
- Network accessline 51
profile: { name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", - Network accessline 52
name: "Alice Smith", avatar: "https://cdn.example.com/alice.jpg", bio: "Developer building cool things" - Network accessline 53
avatar: "https://cdn.example.com/alice.jpg", - Network accessline 412
References: - [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 413
- [Embedding vs Referencing](https://mongodb.com/docs/manual/data-modeling/concepts/embedding-vs-references/) - Network accessline 414
- [Model One-to-One Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-one-relationships-between-documents/) - Network accessline 415
- [Model One-to-Many Relationships with Embedded Documents](https://mongodb.com/docs/manual/tutorial/model-embedded-one-to-many-relationships-between-documents/) - Network accessline 416
- [Model One-to-Many Relationships with References](https://mongodb.com/docs/manual/tutorial/model-referenced-one-to-many-relationships-between-documents/) - Network accessline 417
- [Model Many-to-Many Relationships](https://mongodb.com/docs/manual/tutorial/model-embedded-many-to-many-relationships-between-documents/) - Network accessline 418
- [Model Tree Structures](https://mongodb.com/docs/manual/applications/data-models-tree-structures/)
skills/mongodb-schema-design/references/fundamental-schema-validation.md
- Network accessline 131
Reference: [Schema Validation](https://mongodb.com/docs/manual/core/schema-validation/)
skills/mongodb-schema-design/references/pattern-approximation.md
- Network accessline 80
Reference: [Use the Approximation Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/approximation-schema-pattern/)
skills/mongodb-schema-design/references/pattern-archive.md
- Network accessline 143
Reference: [Archive Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/archive/)
skills/mongodb-schema-design/references/pattern-attribute.md
- Network accessline 77
Reference: [Attribute Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/group-data/attribute-pattern/)
skills/mongodb-schema-design/references/pattern-bucket.md
- Network accessline 12
> **For time-series data**, prefer [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/), which apply bucketing automatically with built-in compression and indexing optimizations. - Network accessline 75
- **Time-series workloads**: Use [Time Series Collections](https://www.mongodb.com/docs/manual/core/timeseries-collections/) instead — they handle bucketing, compression, and indexing automatically. - Network accessline 102
Reference: [Group Data with the Bucket Pattern](https://www.mongodb.com/docs/manual/data-modeling/design-patterns/group-data/bucket-pattern/)
skills/mongodb-schema-design/references/pattern-computed.md
- Network accessline 133
When the computed results are best stored in a separate collection rather than embedded in the source documents, MongoDB's [on-demand materialized views](https://www.mongodb.com/docs/manual/core/materialized-views/) formalize this approach. - Network accessline 162
Reference: [Computed Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/computed-values/computed-schema-pattern/)
skills/mongodb-schema-design/references/pattern-document-versioning.md
- Network accessline 92
The `updatePolicy` function writes to two collections (inserting a revision **and** updating the current document). It may or may not be prudent to wrap the call in a [multi-document transaction](https://mongodb.com/docs/manual/core/transac - Network accessline 166
Reference: [Keep a History of Document Versions](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/document-versioning/)
skills/mongodb-schema-design/references/pattern-extended-reference.md
- Network accessline 72
Reference: [Reduce $lookup Operations](https://mongodb.com/docs/manual/data-modeling/design-antipatterns/reduce-lookup-operations/)
skills/mongodb-schema-design/references/pattern-outlier.md
- Network accessline 159
Reference: [Outlier Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/group-data/outlier-pattern/)
skills/mongodb-schema-design/references/pattern-polymorphic.md
- Network accessline 167
Reference: [Polymorphic Schema Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/polymorphic-data/polymorphic-schema-pattern/)
skills/mongodb-schema-design/references/pattern-schema-versioning.md
- Network accessline 271
References: - [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 272
- [Schema Versioning Pattern](https://mongodb.com/docs/manual/data-modeling/design-patterns/data-versioning/schema-versioning/) - Network accessline 273
- [Schema Validation](https://mongodb.com/docs/manual/core/schema-validation/)
skills/mongodb-schema-design/references/pattern-time-series-collections.md
- Network accessline 190
Reference: [Time Series Collections](https://mongodb.com/docs/manual/core/timeseries-collections/)
skills/mongodb-schema-design/references/source-slow-query-logs.md
- Network accessline 13
If the API call returns auth or access errors, see the [Performance Advisor docs](https://www.mongodb.com/docs/atlas/performance-advisor/). - Network accessline 17
Atlas Admin API endpoint ([query parameters reference](https://www.mongodb.com/docs/ops-manager/current/reference/api/performance-advisor/get-slow-queries/#request-query-parameters)):
skills/mongodb-search-and-ai/scripts/quickstart_complete.py
tools/review-skill/SKILL.md
- Dangerous shell operationline 79
- **macOS**: `curl -fsSL https://claude.ai/install.sh | bash` - Network accessline 79
- **macOS**: `curl -fsSL https://claude.ai/install.sh | bash` - Network accessline 80
- **Other platforms**: follow the [Claude Code quickstart guide](https://code.claude.com/docs/en/quickstart)
tools/review-skill/assets/report.md
tools/review-skill/references/install-skill-validator.md
- Network accessline 42
1. **Claude Code CLI** (`claude --version`) — install with `curl -fsSL https://claude.ai/install.sh | bash` on macOS, or follow the - Dangerous shell operationline 43
`curl -fsSL https://claude.ai/install.sh | bash` on macOS, or follow the - Network accessline 43
`curl -fsSL https://claude.ai/install.sh | bash` on macOS, or follow the - Network accessline 44
[Claude Code quickstart guide](https://code.claude.com/docs/en/quickstart)
tools/review-skill/references/llm-scoring.md
- Network accessline 46
| Error | Cause | Fix | |-------|-------|-----| | `claude: command not found` | CLI not installed | macOS: `curl -fsSL https://claude.ai/install.sh \| bash`; other platforms: [quickstart guide](https://code.claude.com/docs/en/quickstart) | - Network accessline 47
|-------|-------|-----| | `claude: command not found` | CLI not installed | macOS: `curl -fsSL https://claude.ai/install.sh \| bash`; other platforms: [quickstart guide](https://code.claude.com/docs/en/quickstart) | | `claude` auth error | - Dangerous shell operationline 48
| `claude: command not found` | CLI not installed | macOS: `curl -fsSL https://claude.ai/install.sh \| bash`; other platforms: [quickstart guide](https://code.claude.com/docs/en/quickstart) | - Network accessline 48
| `claude: command not found` | CLI not installed | macOS: `curl -fsSL https://claude.ai/install.sh \| bash`; other platforms: [quickstart guide](https://code.claude.com/docs/en/quickstart) | - Network accessline 98
For more context on why novelty matters and how to think about skill quality, refer the SME to: https://agentskillreport.com/ - Network accessline 99
refer the SME to: https://agentskillreport.com/
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Signal confidence: 352 facts
- Network access in prose of the SKILL.mdmongodb-atlas-stream-processing/SKILL.md:116Low confidence
- Network access in prose of the SKILL.mdmongodb-atlas-stream-processing/SKILL.md:204Low confidence
- Network access in prose of the SKILL.mdmongodb-atlas-stream-processing/SKILL.md:205Low confidence
- Network access in prose of the SKILL.mdmongodb-atlas-stream-processing/SKILL.md:206Low confidence
- Network access in prose of documentationmongodb-atlas-stream-processing/references/connection-configs.md:3Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:189Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:190Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:204Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:209Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:214Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:219Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:247Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:248Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/connection-configs.md:249Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/development-workflow.md:98Low confidence
- Network access in code of documentationmongodb-atlas-stream-processing/references/development-workflow.md:100Low confidence
- Network access in prose of documentationmongodb-atlas-stream-processing/references/pipeline-patterns.md:3Low confidence
- Network access in prose of documentationmongodb-atlas-stream-processing/references/sizing-and-parallelism.md:139Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-connection/references/monitoring-guide.md:5Low confidence
- Network access in prose of documentationmongodb-connection/references/monitoring-guide.md:5Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-connection/references/monitoring-guide.md:6Low confidence
- Network access in prose of documentationmongodb-connection/references/monitoring-guide.md:6Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-connection/references/monitoring-guide.md:21Low confidence
- Network access in prose of documentationmongodb-connection/references/monitoring-guide.md:21Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-connection/references/monitoring-guide.md:138Low confidence
- Network access in prose of documentationmongodb-connection/references/monitoring-guide.md:138Low confidence
- Network access in prose of the SKILL.mdplugins/mongodb-atlas/skills/mongodb-query-optimizer/SKILL.md:46Low confidence
- Network access in prose of the SKILL.mdmongodb-query-optimizer/SKILL.md:46Low confidence
- Network access in prose of the SKILL.mdplugins/mongodb-atlas/skills/mongodb-schema-design/SKILL.md:141Low confidence
- Network access in prose of the SKILL.mdmongodb-schema-design/SKILL.md:141Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/antipattern-excessive-lookups.md:84Low confidence
- Network access in prose of documentationmongodb-schema-design/references/antipattern-excessive-lookups.md:84Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/antipattern-unnecessary-collections.md:91Low confidence
- Network access in prose of documentationmongodb-schema-design/references/antipattern-unnecessary-collections.md:91Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/antipattern-unnecessary-indexes.md:95Low confidence
- Network access in prose of documentationmongodb-schema-design/references/antipattern-unnecessary-indexes.md:95Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-document-model.md:91Low confidence
- Network access in prose of documentationmongodb-schema-design/references/fundamental-document-model.md:91Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-document-size.md:223Low confidence
- Network access in prose of documentationmongodb-schema-design/references/fundamental-document-size.md:223Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-document-size.md:224Low confidence
- Network access in prose of documentationmongodb-schema-design/references/fundamental-document-size.md:224Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-document-size.md:225Low confidence
- Network access in prose of documentationmongodb-schema-design/references/fundamental-document-size.md:225Low confidence
- Network access in prose of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-document-size.md:226Low confidence
- Network access in prose of documentationmongodb-schema-design/references/fundamental-document-size.md:226Low confidence
- Network access in code of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-embed-vs-reference.md:51Low confidence
- Network access in code of documentationmongodb-schema-design/references/fundamental-embed-vs-reference.md:51Low confidence
- Network access in code of documentationplugins/mongodb-atlas/skills/mongodb-schema-design/references/fundamental-embed-vs-reference.md:52Low confidence
- Network access in code of documentationmongodb-schema-design/references/fundamental-embed-vs-reference.md:52Low confidence
and 302 more
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Manifest .claude-plugin/marketplace.json | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Native | Manifest gemini-extension.json | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
