Ikram Alam The Evolution Of Todo Mastering Spec Driven Development Cloud Native AI Fetching Library Docs

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:

1. fetch-docs.sh calls fetch-raw.sh (which uses mcp-client.py)
2. Full response (934 tokens) stays in subprocess memory
3. Shell filters (awk/grep/sed) extract essentials (0 LLM tokens used)
4. 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

Verification

Run: python3 scripts/verify.py

Expected: ✓ fetch-docs.sh ready

If Verification Fails

1. Run diagnostic: ls -la scripts/fetch-docs.sh
2. Check: Script exists and is executable
3. Fix: chmod +x scripts/fetch-docs.sh
4. Stop and report if still failing - do not proceed with downstream steps

Troubleshooting

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.

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