Which integrations are available for this server?

Supports utilizing local Hugging Face models for sentence window retrieval and document embedding to provide context for RAG operations. Integrates with OpenAI embedding endpoints and models to index documents and enable high-quality retrieval-augmented generation.

How do I use MCP-RAGNAR?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP-RAGNAR search my local documentation for the API authentication steps" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP-RAGNAR - a local RAG MCP Server

A local MCP server that implements RAG (Retrieval-Augmented Generation) with sentence window retrieval.

Features

Document indexing with support for multiple file types (txt, md, pdf, doc, docx)
Sentence window retrieval for better context understanding
Configurable embedding models (OpenAI or local hugging face mode - i.e BAAI/bge-large-en-v1.5)
MCP server integration for easy querying

Requirements

Python 3.10+
UV package manager

Installation

Clone the repository:

git clone <repository-url> cd mcp-ragnar

Install dependencies using UV:

uv pip install -e .

Usage

Indexing Documents

You can index documents either programmatically or via the command line.

Indexing

python -m indexer.index /path/to/documents /path/to/index # to change the default local embedding model and chunk size python -m indexer.index /path/to/documents /path/to/index --chunk-size=512 --embed-model BAAI/bge-small-en-v1.5 # With OpenAI embedding endpoint (put your OPENAI_API_KEY in env) python -m indexer.index /path/to/documents /path/to/index --embed-endpoint https://api.openai.com/v1 --embed-model text-embedding-3-small --tokenizer-model o200k_base # Get help python -m indexer.index --help

Running the MCP Server

Configuration

can be supplied as env var or .env file

EMBED_ENDPOINT: (Optional) Path to an OpenAI compatible embedding endpoint (ends with /v1). If not set, a local Hugging Face model is used by default.
EMBED_MODEL: (Optional) Name of the embedding model to use. Default value of BAAI/bge-large-en-v1.5.
INDEX_ROOT: The root directory for the index, used by the retriever. This is mandatory for MCP (Multi-Cloud Platform) querying.
MCP_DESCRIPTION: The exposed name and description for the MCP server, used for MCP querying only. This is mandatory for MCP querying. For example: "RAG to my local personal documents"
INDEX_ROOT: the root path of the index

in SSE mode it will listen to http://localhost:8001/ragnar

python server/sse.py

in stdio mode

install locally as an uv tool

uv tool install .

Claude Desktop:

Update the following:

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

On Windows: %APPDATA%/Claude/claude_desktop_config.json

Example :

{ "mcpServers": { "mcp-ragnar": { "command": "uvx", "args": [ "mcp-ragnar" ], "env": { "OPENAI_API_KEY": "", "EMBED_ENDPOINT": "https://api.openai.com/v1", "EMBED_MODEL": "text-embedding-3-small", "MCP_DESCRIPTION": "My local Rust documentation", "INDEX_ROOT": "/tmp/index" } } } }