Schema | rag-retriever-mcp

rag-retriever-mcp

Describes the environment variables required to run the server.

Name	Required	Description	Default
`RAG_EMBED_BACKEND`	No	Embedding backend: local, ollama, or openai	local
`RAG_OPENAI_API_KEY`	No	API key for OpenAI-compatible embedding backend (required if backend is openai)

Features and capabilities supported by this server

Capability	Details
`tools`	{ "listChanged": false }
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`experimental`	{}

Functions exposed to the LLM to take actions

Name	Description
index_pathA	Index a file or a whole folder of documents (pdf, docx, pptx, xlsx, html, md, txt...). Extracts text, chunks it, embeds it, and stores vectors for later search. Returns a summary of how many files/chunks were indexed and what was skipped.
searchA	Search the indexed documents for passages relevant to `query` and return the top `k` chunks (with source path and similarity score). Use these passages as grounding to answer the user's question yourself — this tool does NOT answer.
list_sourcesB	List the documents currently indexed and how many chunks each has.
statsA	Show retriever status: embedding backend/model, storage location, and counts.

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

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