Context7 Efficient Documentation Fetcher

Fetch library documentation with automatic 77% token reduction via shell pipeline.

Quick Start

Always use the token-efficient shell pipeline:

Automatic library resolution + filtering

bash scripts/fetch-docs.sh --library <library-name> --topic <topic>

Examples:

bash scripts/fetch-docs.sh --library react --topic useState bash scripts/fetch-docs.sh --library nextjs --topic routing bash scripts/fetch-docs.sh --library prisma --topic queries

Result: Returns ~205 tokens instead of ~934 tokens (77% savings).

Standard Workflow

For any documentation request, follow this workflow:

1. Identify Library and Topic

Extract from user query:

• Library: React, Next.js, Prisma, Express, etc.

• Topic: Specific feature (hooks, routing, queries, etc.)

2. Fetch with Shell Pipeline

bash scripts/fetch-docs.sh --library <library> --topic <topic> --verbose

The --verbose flag shows token savings statistics.

3. Use Filtered Output

The script automatically:

• Fetches full documentation (934 tokens, stays in subprocess)

• Filters to code examples + API signatures + key notes

• Returns only essential content (205 tokens to Claude)

Parameters

Basic Usage

bash scripts/fetch-docs.sh [OPTIONS]

Required (pick one):

• --library <name>

• Library name (e.g., "react", "nextjs")

• --library-id <id>

• Direct Context7 ID (faster, skips resolution)

Optional:

• --topic <topic>

• Specific feature to focus on

• --mode <code|info>

• code for examples (default), info for concepts

• --page <1-10>

• Pagination for more results

• --verbose

• Show token savings statistics

Mode Selection

Code Mode (default): Returns code examples + API signatures

--mode code

Info Mode: Returns conceptual explanations + fewer examples

--mode info

Common Library IDs

Use --library-id for faster lookup (skips resolution):

React: /reactjs/react.dev Next.js: /vercel/next.js Express: /expressjs/express Prisma: /prisma/docs MongoDB: /mongodb/docs Fastify: /fastify/fastify NestJS: /nestjs/docs Vue.js: /vuejs/docs Svelte: /sveltejs/site

Workflow Patterns

Pattern 1: Quick Code Examples

User asks: "Show me React useState examples"

bash scripts/fetch-docs.sh --library react --topic useState --verbose

Returns: 5 code examples + API signatures + notes (~205 tokens)

Pattern 2: Learning New Library

User asks: "How do I get started with Prisma?"

Step 1: Get overview

bash scripts/fetch-docs.sh --library prisma --topic "getting started" --mode info

Step 2: Get code examples

bash scripts/fetch-docs.sh --library prisma --topic queries --mode code

Pattern 3: Specific Feature Lookup

User asks: "How does Next.js routing work?"

bash scripts/fetch-docs.sh --library-id /vercel/next.js --topic routing

Using --library-id is faster when you know the exact ID.

Pattern 4: Deep Exploration

User needs comprehensive information:

Page 1: Basic examples

bash scripts/fetch-docs.sh --library react --topic hooks --page 1

Page 2: Advanced patterns

bash scripts/fetch-docs.sh --library react --topic hooks --page 2

Token Efficiency

How it works:

• fetch-docs.sh calls fetch-raw.sh (which uses mcp-client.py )

• Full response (934 tokens) stays in subprocess memory

• Shell filters (awk/grep/sed) extract essentials (0 LLM tokens used)

• Returns filtered output (205 tokens) to Claude

Savings:

• Direct MCP: 934 tokens per query

• This approach: 205 tokens per query

• 77% reduction

Do NOT use mcp-client.py directly - it bypasses filtering and wastes tokens.

Advanced: Library Resolution

If library name fails, try variations:

Try different formats

--library "next.js" # with dot --library "nextjs" # without dot --library "next" # short form

Or search manually

bash scripts/fetch-docs.sh --library "your-library" --verbose

Check output for suggested library IDs

Troubleshooting

Issue Solution

Library not found Try name variations or use broader search term

No results Use --mode info or broader topic

Need more examples Increase page: --page 2

Want full context Use --mode info for explanations

References

For detailed Context7 MCP tool documentation, see:

• references/context7-tools.md - Complete tool reference

Implementation Notes

Components (for reference only, use fetch-docs.sh):

• mcp-client.py

• Universal MCP client (foundation)

• fetch-raw.sh

• MCP wrapper

• extract-code-blocks.sh

• Code example filter (awk)

• extract-signatures.sh

• API signature filter (awk)

• extract-notes.sh

• Important notes filter (grep)

• fetch-docs.sh

• Main orchestrator (ALWAYS USE THIS)

Architecture: Shell pipeline processes documentation in subprocess, keeping full response out of Claude's context. Only filtered essentials enter the LLM context, achieving 77% token savings with 100% functionality preserved.

Based on Anthropic's "Code Execution with MCP" blog post.