RAGNet MCP

Give Claude access to any documentation you want.

RAGNet crawls documentation websites, stores them in a searchable database, and lets Claude search through them when you're coding. Instead of copy-pasting docs into your prompts, Claude can look things up itself.

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How It Works (Plain English)

┌─────────────────┐      stdio      ┌─────────────────┐
│   Claude Code   │ ◄─────────────► │   RAGNet MCP    │
│   (or Desktop)  │   (messages)    │   (this tool)   │
└─────────────────┘                 └────────┬────────┘
                                             │
                                    HTTP     │
                                             ▼
                                    ┌─────────────────┐
                                    │     Qdrant      │
                                    │ (vector database)│
                                    └─────────────────┘

Three pieces:

1. Claude (the AI) - asks questions like "how do I use AsyncWebCrawler?"
2. RAGNet MCP (this project) - receives the question, searches the database, returns relevant docs
3. Qdrant (vector database) - stores all the documentation chunks and finds similar content

Important: RAGNet is NOT a web server. It's a subprocess that Claude spawns and talks to via stdin/stdout (like two programs chatting through a pipe). Qdrant is the only actual server running on a port.

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Quick Start

What You'll Need

• Python 3.10 or newer - Download here
• Docker Desktop - Download here (for running Qdrant)
• OpenAI API key - Get one here (for generating embeddings)

Install from PyPI (Recommended)

pip install ragnet-mcp
ragnet init

Install from Source

git clone https://github.com/orro3790/ragnet.git
cd ragnet
pip install -e .
ragnet init

The ragnet init command will:

1. Ask for your OpenAI API key
2. Auto-generate a Qdrant API key
3. Create the .env file
4. Start Qdrant via Docker
5. Create the database collection

Crawl Documentation

ragnet dashboard

This opens the TUI where you can crawl documentation sources. Select a source from crawlist/ and start indexing.

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CLI Reference

Command	Description
ragnet init	First-time setup (API keys, Docker, collection)
ragnet start	Start Qdrant
ragnet stop	Stop Qdrant
ragnet status	Check service status
ragnet dashboard	Launch the TUI
ragnet mcp	Run the MCP server directly
ragnet update	Update to the latest version

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Connect Claude to RAGNet

For Claude Desktop:

Edit your config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "ragnet-mcp": {
      "command": "C:/full/path/to/ragnet/venv/Scripts/python.exe",
      "args": ["C:/full/path/to/ragnet/ragnet.py"],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "QDRANT__SERVICE__API_KEY": "your-qdrant-api-key"
      },
      "alwaysAllow": ["find_code_examples", "get_context_chain", "list_sources", "query"],
      "timeout": 30
    }
  }
}

Note: OPENAI_API_KEY is loaded from the .env file in the ragnet directory. The alwaysAllow list pre-approves these tools so Claude doesn't ask for permission each time.

Restart Claude Desktop to see the RAGNet tools.

For Claude Code:

Add to your project's .mcp.json:

{
  "mcpServers": {
    "ragnet-mcp": {
      "command": "C:/full/path/to/ragnet/venv/Scripts/python.exe",
      "args": ["C:/full/path/to/ragnet/ragnet.py"],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "QDRANT__SERVICE__API_KEY": "your-qdrant-api-key"
      },
      "alwaysAllow": ["find_code_examples", "get_context_chain", "list_sources", "query"],
      "timeout": 30
    }
  }
}

Tip: Use forward slashes in paths even on Windows, or escape backslashes (C:\\Users\\...).

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Using RAGNet

Once connected, Claude can use these tools:

Tool	What it does	When to use it
query	Basic semantic search	Looking up specific things: "AsyncWebCrawler class", "BrowserConfig options"
query_with_hyde	Smarter conceptual search	Asking "how" or "why" questions: "how do I handle authentication?"
find_code_examples	Find code snippets	"Show me examples of using X"
query_complex	Multi-part questions	"What is X and how does it compare to Y?"
get_context_chain	Get surrounding chunks	When you need more context around a result
list_sources	See indexed docs	Check what documentation is available

You don't need to call these manually - just ask Claude questions and it'll use the right tool.

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Adding Your Own Documentation

1. Create a file in crawlist/ named your-docs.md
2. Add URLs (one per line) or a sitemap URL
3. Run ragnet dashboard and select your new source

Example crawlist/my-library.md:

# My Library Docs

https://my-library.dev/docs/getting-started
https://my-library.dev/docs/api-reference
https://my-library.dev/docs/examples

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Troubleshooting

Check status first

ragnet status

This shows if Docker, Qdrant, and the collection are properly configured.

"Qdrant connection refused"

Make sure Docker is running:

ragnet start

"OpenAI API error"

Check that your OPENAI_API_KEY in .env is valid and has credits.

"No results found"

Run ragnet dashboard and crawl some documentation first.

Claude doesn't see the tools

• For Claude Code: Make sure your .mcp.json is configured correctly
• For Claude Desktop: Restart the app after editing config

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Project Structure

ragnet/
├── cli.py                  # Unified CLI (ragnet command)
├── ragnet.py               # The MCP server (what Claude talks to)
├── dashboard.py            # Textual TUI for crawling/management
├── rag_pipeline.py         # Crawls and indexes documentation
├── config.py               # Configuration management
├── chunk_processor.py      # Splits docs into searchable chunks
├── crawlist/               # URL lists for documentation sources
├── qdrant_admin_utils/     # Database management scripts
├── docker-compose.yml      # Qdrant container config
└── venv/                   # Python virtual environment

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How the Search Works (For the Curious)

When you search, RAGNet can use multiple strategies:

1. Dense search (default): Converts your query to a vector and finds similar vectors. Good for semantic matching.

2. Hybrid search: Combines dense vectors with keyword matching (BM25). Better when exact terms matter.

3. HyDE (Hypothetical Document Embeddings): First generates what a good answer would look like, then searches for docs similar to that answer. Better for conceptual questions.

Your Question → [Generate hypothetical answer] → [Search for similar docs] → Results

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Using Qdrant Cloud (Optional)

Instead of running Qdrant locally with Docker, you can use their free cloud tier:

1. Sign up at cloud.qdrant.io
2. Create a cluster
3. Get your URL and API key
4. Run init with --skip-docker:

   ragnet init --skip-docker --qdrant-key your-cloud-api-key
   

5. Update .env with your cloud URL:

   QDRANT_URL=https://your-cluster-id.aws.cloud.qdrant.io
   

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License

MIT