Which integrations are available for this server?

Enables semantic search and retrieval of Google Gemini API documentation, providing detailed information on large language models, function calling, embeddings, and multimodal capabilities.

How do I use Documentation Fetcher & RAG Search?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Documentation Fetcher & RAG Search search the gemini docs for function calling examples" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Documentation Fetcher & RAG Search

A modular system for fetching API documentation and enabling semantic search via RAG (Retrieval-Augmented Generation). Designed to give AI coding assistants like Claude access to up-to-date documentation from any project.

Features

Fetch Documentation: Download complete documentation from API providers in markdown format
Semantic Search: Hybrid search combining vector embeddings with keyword matching
MCP Server: Expose search as tools accessible from Claude Code in any project
Modular Design: Easy to add new documentation sources

Supported Documentation Sources

Source	Documents	Description
Gemini	~2000	Google Gemini API - LLM, function calling, embeddings, multimodal
FastMCP	~1900	FastMCP framework - MCP servers, tools, resources, authentication

Quick Start

Prerequisites

Python 3.12+
Ollama with bge-m3 model
Claude Code (for MCP integration)

Installation

# Clone the repository
git clone <repository-url>
cd documentation

# Create virtual environment
python3.12 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Pull the embedding model
ollama pull bge-m3

Fetch & Index Documentation

# Fetch documentation
python -m src.main fetch gemini
python -m src.main fetch fastmcp

# Index for search (requires Ollama running)
python -m src.rag.index gemini
python -m src.rag.index fastmcp

Search Documentation

# Search Gemini docs
python -m src.main search "function calling"

# Search FastMCP docs
python -m src.main search "how to create a tool" -c fastmcp

# More results
python -m src.main search "rate limits" -n 10

MCP Server Integration

The MCP server exposes documentation search as tools that Claude Code can use from any project.

Install in Claude Code

IMPORTANT: MCP configuration requires absolute paths. The cwd field is NOT supported by Claude Code.

Option 1: Using Claude CLI (recommended)

# Replace /path/to/documentation with your actual absolute path
claude mcp add docs-search --scope user --transport stdio -- \
  /path/to/documentation/.venv/bin/python \
  /path/to/documentation/src/mcp_server.py

Option 2: Add to ~/.claude.json manually

{
  "mcpServers": {
    "docs-search": {
      "command": "/path/to/documentation/.venv/bin/python",
      "args": ["/path/to/documentation/src/mcp_server.py"]
    }
  }
}

Common mistakes to avoid:

Do NOT use cwd - it's not a valid MCP configuration field
Do NOT use relative paths - they resolve from the caller's directory
Do NOT use -m src.mcp_server - this requires being in the project directory

Verify Installation

# Check server is registered
claude mcp list

# In Claude Code, check connection status
/mcp

Available Tools

Tool	Description
`search_docs(query, collection, num_results)`	Search documentation with hybrid semantic + keyword search
`list_collections()`	List available documentation collections

Available Resources

Resource URI	Description
`docs://collections`	JSON list of all collections
`docs://gemini/pages`	List of all Gemini documentation pages
`docs://fastmcp/pages`	List of all FastMCP documentation pages
`docs://gemini/search-help`	Search tips for Gemini docs
`docs://fastmcp/search-help`	Search tips for FastMCP docs

Usage from Claude Code

Once installed, you can ask Claude from any project:

"Search the gemini docs for function calling"
"What documentation collections are available?"
"Search fastmcp for how to create tools"
"Find rate limit information in gemini docs"

Project Structure

documentation/
├── src/
│   ├── main.py                 # CLI entry point
│   ├── mcp_server.py           # MCP server for Claude Code
│   ├── core/
│   │   ├── fetcher.py          # HTTP/markdown fetching
│   │   └── parser.py           # Navigation parsing
│   ├── modules/
│   │   ├── base.py             # Abstract base class
│   │   ├── gemini/             # Gemini documentation module
│   │   └── fastmcp/            # FastMCP documentation module
│   └── rag/
│       ├── chunker.py          # Markdown-aware chunking
│       ├── embedder.py         # Ollama bge-m3 embeddings
│       ├── sqlite_store.py     # SQLite + sqlite-vec vector store
│       ├── search.py           # Hybrid search with RRF
│       ├── query_expander.py   # Multi-query expansion (LLM)
│       ├── reranker.py         # Cross-encoder reranking
│       └── index.py            # Indexing CLI
├── output/                     # Fetched documentation
│   ├── gemini/
│   └── fastmcp/
├── data/
│   └── docs.db                 # SQLite vector database
├── requirements.txt
└── README.md

Adding New Documentation Sources

Create a new module in src/modules/<name>/:

# src/modules/example/config.py
BASE_URL = "https://docs.example.com"
SITEMAP_URL = "https://docs.example.com/sitemap.xml"
MARKDOWN_SUFFIX = ".md"  # or ".md.txt" for Google sites

# src/modules/example/module.py
from src.modules.base import BaseModule

class ExampleModule(BaseModule):
    @property
    def name(self) -> str:
        return "example"

    def get_doc_urls(self) -> list[NavLink]:
        # Parse sitemap or navigation
        ...

    def fetch_page(self, url: str) -> str:
        # Fetch markdown content
        ...

from src.modules.example.module import ExampleModule

# In fetch_command():
elif args.module == "example":
    module = ExampleModule()
    module.run(output_dir)

Add to KNOWN_COLLECTIONS in src/mcp_server.py
Fetch and index:

python -m src.main fetch example
python -m src.rag.index example

How It Works

Fetching

Parse navigation/sitemap to discover documentation pages
Fetch each page in markdown format (using source-specific tricks like .md.txt suffix)
Save with source URL metadata

Indexing

Chunk markdown by headers (preserving code blocks)
Generate embeddings via Ollama bge-m3 (1024 dimensions)
Store in SQLite with sqlite-vec (vectors) and FTS5 (keywords)

Searching

Generate query embedding
Perform semantic search (sqlite-vec vector similarity)
Perform keyword search (FTS5 BM25)
Combine with Reciprocal Rank Fusion (RRF)
Optionally expand query with LLM variations
Optionally rerank with cross-encoder
Return ranked results with source URLs

Configuration

Environment Variables

Variable	Description	Default
`OLLAMA_HOST`	Ollama server URL	`http://localhost:11434`

SQLite Database

Vector database stored in data/docs.db. Each documentation source gets its own collection within the database.

Development

# Run tests
python -m pytest

# Check MCP server
claude mcp list

# Test search functionality
python -m src.rag.search

Troubleshooting

"Ollama connection failed"

# Make sure Ollama is running
ollama serve

# Pull the embedding model
ollama pull bge-m3

"No results found"

# Check if collection is indexed
python -m src.rag.index --status gemini

# Re-index if needed
python -m src.rag.index --clear gemini

MCP server not connecting

# Check server status
claude mcp list

# Reinstall
claude mcp remove docs-search
fastmcp install claude-code src/mcp_server.py --name docs-search

License

MIT

Credits

Ollama - Local LLM and embeddings
sqlite-vec - Vector search for SQLite
FastMCP - MCP server framework

Documentation Fetcher & RAG Search

Documentation Fetcher & RAG Search

Features

Supported Documentation Sources

Quick Start

Prerequisites

Installation

Fetch & Index Documentation

Search Documentation

MCP Server Integration

Install in Claude Code

Verify Installation

Available Tools

Available Resources

Usage from Claude Code

Project Structure

Adding New Documentation Sources

How It Works

Fetching

Indexing

Searching

Configuration

Environment Variables

SQLite Database

Development

Troubleshooting

"Ollama connection failed"

"No results found"

MCP server not connecting

License

Credits

Resources

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