MCP Generix

MCP Generix

Provides semantic search and management of shared documentation using ChromaDB and OpenAI embeddings. It enables users to query local documents by meaning, list files, and read content through natural language tools.

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README

MCP Generix — Shared Documentation with Semantic Search

Custom MCP server that provides semantic search over documents in the docs/ folder. Uses ChromaDB for vector storage and OpenAI embeddings.

Setup

  1. Clone this repo
  2. Create a virtual environment and install dependencies:
    cd mcp_generix
python3 -m venv .venv
source .venv/bin/activate
pip install "mcp[cli]" chromadb openai
  3. Set your OpenAI API key:
    export OPENAI_API_KEY="your-key-here"
  4. Add the MCP server to Claude Code:
    claude mcp add generix-docs -- /path/to/mcp_generix/.venv/bin/python /path/to/mcp_generix/server.py

Adding / Removing Documents

  1. Add markdown (.md) or text files to the docs/ folder
  2. Commit and push
  3. Other team members pull to get the latest documents
  4. The server re-indexes documents automatically on startup, or use the reindex_docs tool

Available Tools

Tool Description
search_docs Semantic search — find relevant passages by meaning, not just keywords
list_docs List all documents in the docs folder
read_doc Read the full contents of a specific document
reindex_docs Re-index documents after adding/removing files

Folder Structure

mcp_generix/
├── server.py          ← MCP server with semantic search
├── pyproject.toml     ← Python dependencies
├── docs/              ← Shared documentation (managed via git)
│   └── (your documents here)
├── .chroma/           ← ChromaDB vector store (gitignored, local)
└── .venv/             ← Python virtual environment (gitignored, local)

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