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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.

Updated
Feb 17, 2026

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

ToolDescription
search_docsSemantic search — find relevant passages by meaning, not just keywords
list_docsList all documents in the docs folder
read_docRead the full contents of a specific document
reindex_docsRe-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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