mcp-doc-search

A small Model Context Protocol (MCP) server that gives an LLM grounded search and retrieval over a local folder of documents. The model calls search_docs to find relevant sources, then get_document to read and cite them, so answers stay grounded in your files instead of the model's memory.

Built as a focused, forward-deployed pattern: the simplest thing that makes retrieval real and citable, ready to drop into an enterprise workflow and extend with embeddings or evals.

Tools

Related MCP server: RAG Database MCP Server

Quick start

npm install
npm run build
DOCS_DIR=./docs npm start

The server speaks MCP over stdio, so it is launched by an MCP client rather than used directly.

Use with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "doc-search": {
      "command": "node",
      "args": ["/absolute/path/to/mcp-doc-search/dist/index.js"],
      "env": { "DOCS_DIR": "/absolute/path/to/your/docs" }
    }
  }
}

Then ask Claude something answerable from your docs, for example: "Search the docs for the KYC retry policy and cite the source."

How it works

1. On startup the server indexes every .md / .txt file in DOCS_DIR.

2. search_docs scores documents with a term-frequency relevance measure and returns ranked snippets.

3. get_document returns full text for citation.

Scoring is intentionally simple. The point is the grounded retrieval contract, which is swappable for vector embeddings without changing the tool interface.

Roadmap

• Vector embeddings + chunking for semantic search

• PDF ingestion with OCR for scanned documents

• A small eval set to measure answer groundedness

License

MIT