Local Rag MCP server

MCP Local RAG

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Search private documents from an MCP client or the terminal without sending them to an embedding API.

mcp-local-rag indexes PDF, DOCX, Markdown, and text files on your machine. Search combines semantic similarity with keyword matching, so queries can match both intent and exact technical terms such as API names, class names, and error codes.

Features

After the initial model download, text ingestion and search work offline.

• Runs locally: Document parsing, embeddings, storage, and search run on your machine.

exact technical terms.

• Hybrid search: Semantic retrieval finds related concepts, while keyword matching boosts

domain of your documents.

• Configurable embeddings: Choose a Hugging Face embedding model that fits the language and

counts. Markdown code blocks stay intact.

• Semantic chunking: Documents are split at topic boundaries instead of fixed character

• MCP and CLI: Use the same index from an AI coding tool or directly from the terminal.

No API key, Docker, Python, or external database is required.

Quick Start

Requirements

• Node.js 22 or later
• Internet access on first use to download the npm package and embedding model
• A directory containing the documents you want to search

Set BASE_DIR to that directory. It is also the security boundary for file operations. Replace /absolute/path/to/your/documents below with the directory's absolute path.

mcp-local-rag uses the standard MCP protocol over a local stdio server, so it works with AI coding tools and other MCP hosts that support local MCP servers.

Use one of the examples below, or register npx -y mcp-local-rag and set BASE_DIR using your client's MCP configuration format.

For Claude Code: Run this command:

claude mcp add local-rag --scope user --env BASE_DIR=/absolute/path/to/your/documents -- npx -y mcp-local-rag

For Codex: Add to ~/.codex/config.toml:

[mcp_servers.local-rag]
command = "npx"
args = ["-y", "mcp-local-rag"]

[mcp_servers.local-rag.env]
BASE_DIR = "/absolute/path/to/your/documents"

For OpenCode: Add to ~/.config/opencode/opencode.json (or opencode.jsonc):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "local-rag": {
      "type": "local",
      "command": ["npx", "-y", "mcp-local-rag"],
      "environment": {
        "BASE_DIR": "/absolute/path/to/your/documents"
      }
    }
  }
}

For Cursor: Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "local-rag": {
      "command": "npx",
      "args": ["-y", "mcp-local-rag"],
      "env": {
        "BASE_DIR": "/absolute/path/to/your/documents"
      }
    }
  }
}

Restart the client, then ask it to build the index:

Sync all documents in the configured root and wait until it finishes.

The first sync downloads the default embedding model (about 90 MB) and may take 1–2 minutes before ingestion starts. Later runs use the local cache.

Once the sync completes:

What does the API documentation say about authentication?

CLI Quick Start

To use the CLI without an MCP client:

Shortened. The full README is on GitHub.