fetching-library-docs

What this skill does

# 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).