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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

Related MCP server: Qdrant MCP Server

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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maintenance

Maintenance

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Response time
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