MCP server
by hannesrudolph
Qdrant Docs Rag offers real-time documentation context using vector-based search and retrieval with Qdrant for efficient
Provides vector-based search across stored documentation to augment AI responses with relevant context. Uses Qdrant for semantic document retrieval.
Qdrant Docs Rag is a community-built MCP server published by hannesrudolph that provides AI assistants with tools and capabilities via the Model Context Protocol. Qdrant Docs Rag offers real-time documentation context using vector-based search and retrieval with Qdrant for efficient It is categorized under ai ml, developer tools.
You can install Qdrant Docs Rag in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Qdrant Docs Rag is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Qdrant Docs Rag is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Qdrant Docs Rag is the kind of server we cite when onboarding engineers to host + tool permissions.
I recommend Qdrant Docs Rag for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Qdrant Docs Rag reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired Qdrant Docs Rag into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Qdrant Docs Rag is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend Qdrant Docs Rag for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Qdrant Docs Rag benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
According to our notes, Qdrant Docs Rag benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
I recommend Qdrant Docs Rag for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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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.
<a href="https://glama.ai/mcp/servers/54hsrjhmq9"><img width="380" height="200" src="https://glama.ai/mcp/servers/54hsrjhmq9/badge" alt="mcp-ragdocs MCP server" /></a>
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 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 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 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 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.
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.
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.
The RAG Documentation tool is designed for:
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 generationQDRANT_URL: URL of your Qdrant vector database instanceQDRANT_API_KEY: API key for authenticating with QdrantThis 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.
This project is a fork of qpd-v/mcp-ragdocs, originally developed by qpd-v. The original project provided the foundation for this implementation.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.