Which integrations are available for this server?

Provides tools for listing, reading, and performing semantic searches on Markdown documentation files. Integrates with OpenAI's embedding models to power semantic search capabilities, allowing for meaning-based retrieval of documentation passages.

How do I use MCP Generix?

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 Generix search the docs for our team's coding standards and best practices" 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 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

Clone this repo
Create a virtual environment and install dependencies:
cd mcp_generix python3 -m venv .venv source .venv/bin/activate pip install "mcp[cli]" chromadb openai
Set your OpenAI API key:
export OPENAI_API_KEY="your-key-here"
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

Add markdown (.md) or text files to the docs/ folder
Commit and push
Other team members pull to get the latest documents
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)