RAGDocs (Vector Documentation Search)

by jumasheff

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About

Enables semantic documentation search and retrieval using vector databases, allowing for URL extraction, source management, and indexing queues with support for multiple embedding providers including Ollama and OpenAI.

Details

Author: jumasheff
Repository: jumasheff/mcp-ragdoc-fork
License: MIT License
Categories: Developer Tools, Design, Workplace, File Management, AI, Search, Knowledge Base, Frontend, Infrastructure
Tags: #web

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name RAGDocs (Vector Documentation Search)

Command (node, npx, python, etc.) npx
Arguments
- Argument 1 -y
- Argument 2 @hannesrudolph/mcp-ragdocs
Environment
- QDRANT_URL
- OPENAI_API_KEY
- QDRANT_API_KEY
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "/c",
                "npx",
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "/c",
                "npx",
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "/c",
                "npx",
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "/c",
                "npx",
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "/c",
                "npx",
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

From the repository

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "rag-docs": {
      "command": "npx",
      "args": [
        "-y",
        "@hannesrudolph/mcp-ragdocs"
      ],
      "env": {
        "OPENAI_API_KEY": "",
        "QDRANT_URL": "",
        "QDRANT_API_KEY": ""
      }
    }
  }
}

You'll need to provide values for the following environment variables:
- OPENAI_API_KEY: Your OpenAI API key for embeddings generation
- QDRANT_URL: URL of your Qdrant vector database instance
- QDRANT_API_KEY: API key for authenticating with Qdrant

`search_documentation`

Search through stored documentation using natural language queries. Returns matching excerpts with context, ranked by relevance. Inputs: query (string), limit (number, optional)

`list_sources`

List all documentation sources currently stored in the system. Returns a comprehensive list of all indexed documentation including source URLs, titles, and last update times.

`extract_urls`

Extract and analyze all URLs from a given web page. This tool crawls the specified webpage, identifies all hyperlinks, and optionally adds them to the processing queue. Inputs: url (string), add_to_queue (boolean, optional)

`remove_documentation`

Remove specific documentation sources from the system by their URLs. The removal is permanent and will affect future search results. Inputs: urls (string[])

`list_queue`

List all URLs currently waiting in the documentation processing queue. Shows pending documentation sources that will be processed when run_queue is called.

`run_queue`

Process and index all URLs currently in the documentation queue. Each URL is processed sequentially, with proper error handling and retry logic.

`clear_queue`

Remove all pending URLs from the documentation processing queue. Use this to reset the queue when you want to start fresh or cancel pending processing.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "ragdocs (vector documentation search)": {
            "env": {
                "QDRANT_URL": "",
                "OPENAI_API_KEY": "",
                "QDRANT_API_KEY": ""
            },
            "args": [
                "-y",
                "@hannesrudolph/mcp-ragdocs"
            ],
            "command": "npx"
        }
    }
}

Linux

{
    "env": {
        "QDRANT_URL": "",
        "OPENAI_API_KEY": "",
        "QDRANT_API_KEY": ""
    },
    "args": [
        "-y",
        "@hannesrudolph/mcp-ragdocs"
    ],
    "command": "npx"
}

Macos

{
    "env": {
        "QDRANT_URL": "",
        "OPENAI_API_KEY": "",
        "QDRANT_API_KEY": ""
    },
    "args": [
        "-y",
        "@hannesrudolph/mcp-ragdocs"
    ],
    "command": "npx"
}

Windows

{
    "env": {
        "QDRANT_URL": "",
        "OPENAI_API_KEY": "",
        "QDRANT_API_KEY": ""
    },
    "args": [
        "/c",
        "npx",
        "-y",
        "@hannesrudolph/mcp-ragdocs"
    ],
    "command": "cmd"
}

RAG Documentation MCP Server

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Features

Tools

search_documentation

Search through stored documentation using natural language queries. Returns matching excerpts with context, ranked by relevance.

Inputs:
- query (string): The text to search for in the documentation. Can be a natural language query, specific terms, or code snippets.
- limit (number, optional): Maximum number of results to return (1-20, default: 5). Higher limits provide more comprehensive results but may take longer to process.

list_sources

List all documentation sources currently stored in the system. Returns a comprehensive list of all indexed documentation including source URLs, titles, and last update times. Use this to understand what documentation is available for searching or to verify if specific sources have been indexed.

extract_urls

Extract and analyze all URLs from a given web page. This tool crawls the specified webpage, identifies all hyperlinks, and optionally adds them to the processing queue.

Inputs:
- url (string): The complete URL of the webpage to analyze (must include protocol, e.g., https://). The page must be publicly accessible.
- add_to_queue (boolean, optional): If true, automatically add extracted URLs to the processing queue for later indexing. Use with caution on large sites to avoid excessive queuing.

remove_documentation

Remove specific documentation sources from the system by their URLs. The removal is permanent and will affect future search results.

Inputs:
- urls (string[]): Array of URLs to remove from the database. Each URL must exactly match the URL used when the documentation was added.

list_queue

List all URLs currently waiting in the documentation processing queue. Shows pending documentation sources that will be processed when run_queue is called. Use this to monitor queue status, verify URLs were added correctly, or check processing backlog.

run_queue

Process and index all URLs currently in the documentation queue. Each URL is processed sequentially, with proper error handling and retry logic. Progress updates are provided as processing occurs. Long-running operations will process until the queue is empty or an unrecoverable error occurs.

clear_queue

Remove all pending URLs from the documentation processing queue. Use this to reset the queue when you want to start fresh, remove unwanted URLs, or cancel pending processing. This operation is immediate and permanent - URLs will need to be re-added if you want to process them later.

Usage

The RAG Documentation tool is designed for:

- Enhancing AI responses with relevant documentation
- Building documentation-aware AI assistants
- Creating context-aware tooling for developers
- Implementing semantic documentation search
- Augmenting existing knowledge bases

Configuration

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "rag-docs": {
      "command": "npx",
      "args": [
        "-y",
        "@hannesrudolph/mcp-ragdocs"
      ],
      "env": {
        "OPENAI_API_KEY": "",
        "QDRANT_URL": "",
        "QDRANT_API_KEY": ""
      }
    }
  }
}

TODO

- Make embeddings configurable to allow choosing between OpenAI embeddings or local Ollama for vector generation

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Acknowledgments

This project is a fork of qpd-v/mcp-ragdocs, originally developed by qpd-v. The original project provided the foundation for this implementation.

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About

Details

Jump to

Setting up with Highlight

From the repository

search_documentation

list_sources

extract_urls

remove_documentation

list_queue

run_queue

clear_queue

Claude Desktop / Cursor

Linux

Macos

Windows

RAG Documentation MCP Server

Features

Tools

search_documentation

list_sources

extract_urls

remove_documentation

list_queue

run_queue

clear_queue

Usage

Configuration

Usage with Claude Desktop

TODO

License

Acknowledgments

Install

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`search_documentation`

`list_sources`

`extract_urls`

`remove_documentation`

`list_queue`

`run_queue`

`clear_queue`