home / skills / mjunaidca / mjs-agent-skills / fetching-library-docs
This skill fetches library documentation with token-efficient filtering, delivering code examples, API signatures, and notes for React, Next.js, Prisma.
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---
name: fetching-library-docs
description: |
Token-efficient library API documentation fetcher using Context7 MCP with 77% token savings.
Fetches code examples, API references, and usage patterns for published libraries (React,
Next.js, Prisma, etc). Use when users ask "how do I use X library", need code examples,
want API syntax, or are learning a framework's official API. Triggers: "Show me React hooks",
"Prisma query syntax", "Next.js routing API". NOT for exploring repo internals/source code
(use researching-with-deepwiki) or local files.
---
# Context7 Efficient Documentation Fetcher
Fetch library documentation with automatic 77% token reduction via shell pipeline.
## Quick Start
**Always use the token-efficient shell pipeline:**
```bash
# 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
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
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
```bash
--mode code
```
**Info Mode:** Returns conceptual explanations + fewer examples
```bash
--mode info
```
## Common Library IDs
Use `--library-id` for faster lookup (skips resolution):
```bash
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
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?"
```bash
# 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
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:
```bash
# 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:
```bash
# 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
| 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 |
| Permission denied | Run: `chmod +x scripts/*.sh` |
## References
For detailed Context7 MCP tool documentation, see:
- [references/context7-tools.md](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](https://www.anthropic.com/engineering/code-execution-with-mcp).
This skill is a token-efficient documentation fetcher that returns concise code examples, API signatures, and usage notes for published libraries (React, Next.js, Prisma, etc.). It uses a Context7 MCP-backed shell pipeline to reduce LLM input by about 77%, delivering compact, focused docs for quick consumption. Use it when you need precise API syntax, runnable examples, or focused guidance without large context payloads.
Identify the library and topic from the user query, then run the orchestrator script which invokes the MCP client in a subprocess. Shell filters extract only code blocks, API signatures, and key notes; the full raw response never enters the LLM context. The result is a drastically smaller payload (~205 tokens) that preserves the actionable content developers need.
What does the script return?
Filtered output containing code examples, API signatures, and concise notes tailored to the requested topic.
How does it save tokens?
The raw MCP response is processed in a subprocess and filtered by shell tools; only the extracted essentials are returned to the LLM, reducing input size by ~77%.