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Context7

skill-petbrains-mvp-builder-context7 · by petbrains

Up-to-date library documentation retrieval using Context7 MCP tools. Process THINK → RESOLVE → FETCH → APPLY. Use when fetching library docs, resolving package names to IDs, getting implementation guides, exploring API references. Provides package resolution strategy, trust score evaluation, token scaling (3K-20K), topic selection patterns.

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Install

$ agentstack add skill-petbrains-mvp-builder-context7

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Context7 Documentation Retrieval

Framework for using /mcp__context7__resolve-library-id and /mcp__context7__get-library-docs tools effectively.

Process: THINK → RESOLVE → FETCH → APPLY

Command: /mcp__context7__resolve-library-id

Resolves a package name to a Context7-compatible library ID.

Parameters

  • libraryName (required): Library name to search for

Returns

List of matching libraries (10-30+ results) with:

  • Context7-compatible library ID: Format /org/project
  • Trust Score: Authority indicator (1-10, higher is better)
  • Code Snippets: Number of available examples
  • Description: Library summary

Selection Process

Priority order:

  1. Name similarity (exact matches always win)
  2. Trust score (minimum 7 preferred, 9-10 ideal)
  3. Documentation coverage (higher snippet counts)
  4. Description relevance to query intent

Decision rules:

  • Exact name + trust ≥7 → Select it
  • Multiple high-trust (≥9) → Choose highest snippet count
  • Low trust (<7) → Prioritize snippet count
  • Ambiguous → Request user clarification

Skip resolve when:

  • User provides path like /vercel/next.js
  • Query contains /org/project format

Always resolve when:

  • User mentions name only: "React", "Next.js"
  • Generic terms: "React docs"

Common Patterns

"react" → /reactjs/react.dev (trust: 10)
"next.js" → /vercel/next.js (trust: 9.5)
"vue" → /vuejs/core (trust: 10)
"mongodb" → /mongodb/docs (trust: 9.8)

Command: /mcp__context7__get-library-docs

Fetches documentation using exact Context7-compatible library ID.

Parameters

  • context7CompatibleLibraryID (required): Exact ID from resolve step
  • topic (optional): Specific focus area
  • tokens (optional): Max tokens (default: 10000, range: 1000-20000)

Token Strategy

  • 3K-5K: Quick reference (single function/method)
  • 6K-10K: Feature exploration
  • 10K-15K: Implementation guides
  • 15K-20K: Comprehensive learning

Topic Selection

Be specific with multi-word topics:

  • ❌ "api" → ✅ "REST API endpoints"
  • ❌ "hooks" → ✅ "useState useEffect lifecycle"
  • ❌ "css" → ✅ "responsive design breakpoints"

Workflows

React Implementation

THINK: Need React infinite scroll docs
RESOLVE: /mcp__context7__resolve-library-id libraryName="react"
SELECT: /reactjs/react.dev (trust: 10)
FETCH: /mcp__context7__get-library-docs context7CompatibleLibraryID="/reactjs/react.dev" topic="infinite scroll virtualization" tokens=12000

Next.js Debugging

THINK: Debug hydration errors
RESOLVE: /mcp__context7__resolve-library-id libraryName="next.js"
SELECT: /vercel/next.js (trust: 9.5)
FETCH: /mcp__context7__get-library-docs context7CompatibleLibraryID="/vercel/next.js" topic="hydration errors debugging SSR" tokens=15000

Direct ID Usage

THINK: User provided /mongodb/docs
FETCH: /mcp__context7__get-library-docs context7CompatibleLibraryID="/mongodb/docs" topic="aggregation pipeline" tokens=15000

Error Handling

No matches: Try alternative names (vue.js → vue) Multiple matches: Ask user preference Wrong library: Re-run with different term Insufficient docs: Increase tokens or refine topic

Best Practices

  1. Think before executing - What does user really need?
  2. Resolve first - Unless user provides /org/project
  3. Be specific with topics - Multi-word topics work better
  4. Scale tokens appropriately - Don't always use maximum
  5. Chain related fetches - Build complete context
  6. Trust score matters - Prefer libraries with scores ≥7

Quick Reference

  1. THINK: What library and documentation needed?
  2. RESOLVE: /mcp__context7__resolve-library-id libraryName="[package]"
  3. SELECT: Based on trust, snippets, relevance
  4. FETCH: /mcp__context7__get-library-docs context7CompatibleLibraryID="[id]" topic="[specific]" tokens="[appropriate]"
  5. APPLY: Use documentation to answer question

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.