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)

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.

Features and capabilities supported by this server

Protocol revision2025-11-25

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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/code-lawyer/rag-retriever'

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