What are the key features of Ragdocs?

Key features include vector-based documentation search and retrieval, support for multiple documentation sources, local (Ollama) and OpenAI embeddings generation, automated documentation processing, and real-time context augmentation for LLMs.

Ragdocs is a tool that augments AI responses by retrieving and processing information from your documentation using vector-based search. It provides real-time context to LLMs, improving the accuracy and relevance of their answers.

What credentials does it need?

It reads `EMBEDDINGS_PROVIDER`, `QDRANT_URL`, `QDRANT_API_KEY`, `OPENAI_API_KEY`, `OLLAMA_BASE_URL`, `OLLAMA_URL` from the environment. Store these in your client's env block or a secrets manager — never commit them to a repository — and grant the narrowest scope that still covers your use case.

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Ragdocs MCP Server

Augment AI responses with context by retrieving and processing documentation through vector search.

Remote serverstreamable-http★ 30

What is the Ragdocs MCP Server?

Augment AI responses with context by retrieving and processing documentation through vector search.

What the server does

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.

Vector-based documentation search and retrieval
Support for multiple documentation sources
Support for local (Ollama) embeddings generation or OPENAI
Semantic search capabilities
Automated documentation processing
Real-time context augmentation for LLMs

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

Recorded tools

Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.

Tool	What it does
search_documentation	Search through stored documentation using natural language queries. Returns matching excerpts with context, ranked by relevance.
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
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.
remove_documentation	Remove specific documentation sources from the system by their URLs. The removal is permanent and will affect future search results.
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 correct
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
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 - U

Configuration

Variable	Description	Required
EMBEDDINGS_PROVIDER	Configuration value read at startup.	Optional
QDRANT_URL	Endpoint or connection string the server talks to.	Yes
QDRANT_API_KEY	Credential the server authenticates with.	Yes
OPENAI_API_KEY	Credential the server authenticates with.	Yes
OLLAMA_BASE_URL	Endpoint or connection string the server talks to.	Yes
OLLAMA_URL	Endpoint or connection string the server talks to.	Yes

Example prompts to try

“Use Ragdocs to search documentation.”
“Use Ragdocs to list sources.”
“Use Ragdocs to extract urls.”

Frequently asked questions

Ragdocs supports various documentation sources including web pages, markdown files, and more. You can add URLs to a processing queue for automated indexing.