RAG for your documents

@devilsdev/rag-mcp-server

An MCP server for your documents. Point it at a folder and Claude, or any other MCP client, can search the documents and check answers against them:

npx @devilsdev/rag-mcp-server ./docs

It indexes the folder when it starts, with the offline TF-IDF embedder and an in-memory retriever from @devilsdev/rag-pipeline-utils. It needs no API key, no database and no network. The assistant is the language model, so the server never calls one.

Requires Node.js 22 or later.

Connect a client

The server speaks MCP over stdio, so the client starts it. Use an absolute path to your folder.

Claude Code:

claude mcp add docs -- npx -y @devilsdev/rag-mcp-server /absolute/path/to/docs

Claude Desktop, Cursor and other clients that use an mcpServers config (for Claude Desktop: Settings → Developer → Edit Config, then restart):

{  "mcpServers": {    "docs": {      "command": "npx",      "args": ["-y", "@devilsdev/rag-mcp-server", "/absolute/path/to/docs"]    }  }}

Then ask something your documents answer, such as "How many vacation days do employees get?".

Tools

search_documents

Returns { passages: [{ id, source, score, text }] }, best first, as structured content and as JSON text. source is the file relative to the folder and id is <source>#<chunk number>. Passages that share no terms with the query are left out.

check_answer

Runs the library's checkAnswer and returns { grounded, groundedness, unsupported, citations, sources }: whether every sentence is supported, the share of supported sentences (0 to 1), the unsupported sentences, the ids of the sources supporting each sentence, and the sources that were checked. The check is lexical and makes no model calls, so a correct paraphrase can come back unsupported; treat it as a flag to look again, not a verdict.

Both tools are read-only. Passage text is content from your files, and the tool descriptions tell the client to treat it as data, not instructions. A file can still contain text written to manipulate an assistant, so index folders you trust.

Files

Markdown (.md, .markdown), HTML (.html, .htm, visible text only), CSV (.csv, one passage per row) and text (.txt) files, in the folder and its subfolders, handled the way the library's loaders handle them; prose is split into sentence chunks of up to 500 characters. Other files, node_modules and .git are skipped. The index lives in memory and is rebuilt each time the server starts, so restart the client's server after changing the documents.

Options

rag-mcp-server <folder> [--top-k <n>] [--embedder local]

Everything the server logs goes to stderr; stdout carries the protocol.

Build your own

For other embedders (OpenAI, Ollama), a persistent index or a vector database, start from the MCP server example, which builds the same kind of server from the library in about 100 lines.

Documentation: https://devilsdev.github.io/rag-pipeline-utils/