MCP-RAGNAR

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

- 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

git clone <repository-url> cd mcp-ragnar

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

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

- 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 tohttp://localhost:8001/ragnar

{ "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" } } } }

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