RAG Documentation MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| QDRANT_URL | Yes | URL of your Qdrant vector database instance | |
| OPENAI_API_KEY | Yes | Your OpenAI API key for embeddings generation | |
| QDRANT_API_KEY | Yes | API key for authenticating with Qdrant |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_documentationA | Search through stored documentation using natural language queries. Use this tool to find relevant information across all stored documentation sources. Returns matching excerpts with context, ranked by relevance. Useful for finding specific information, code examples, or related documentation. |
| list_sourcesA | 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_urlsA | 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. Useful for discovering related documentation pages, API references, or building a documentation graph. Handles various URL formats and validates links before extraction. |
| remove_documentationA | Remove specific documentation sources from the system by their URLs. Use this tool to clean up outdated documentation, remove incorrect sources, or manage the documentation collection. The removal is permanent and will affect future search results. Supports removing multiple URLs in a single operation. |
| list_queueA | 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. Returns URLs in the order they will be processed. |
| run_queueA | 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. Use this after adding new URLs to ensure all documentation is indexed and searchable. Long-running operations will process until the queue is empty or an unrecoverable error occurs. |
| clear_queueA | 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. Returns the number of URLs that were cleared from the queue. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose with no overlap: clear_queue manages queue state, extract_urls discovers links, list_queue and list_sources provide status views, remove_documentation deletes sources, run_queue processes the queue, and search_documentation queries content. The descriptions reinforce these boundaries, making misselection unlikely.
All tool names follow a consistent verb_noun pattern using snake_case, such as clear_queue, extract_urls, list_queue, list_sources, remove_documentation, run_queue, and search_documentation. This uniformity makes the set predictable and easy for agents to navigate.
With 7 tools, the server is well-scoped for its RAG documentation purpose, covering queue management, source discovery, processing, and search. Each tool earns its place without bloat, aligning with typical server sizes of 3-15 tools for focused functionality.
The tool set covers core RAG documentation workflows including queue management, source extraction, indexing, and search, with no dead ends. A minor gap exists in lacking tools for updating or modifying existing documentation sources, but agents can work around this by re-adding URLs.