MCP Generix
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Generixsearch 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 openaiSet 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
Related MCP server: Qdrant MCP Server
Adding / Removing Documents
Add markdown (
.md) or text files to thedocs/folderCommit and push
Other team members pull to get the latest documents
The server re-indexes documents automatically on startup, or use the
reindex_docstool
Available Tools
Tool | Description |
| Semantic search — find relevant passages by meaning, not just keywords |
| List all documents in the docs folder |
| Read the full contents of a specific document |
| 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)This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- -license-quality-maintenanceEnables LLMs to perform semantic search and document management using ChromaDB, supporting natural language queries with intuitive similarity metrics for retrieval augmented generation applications.
- Alicense-qualityBmaintenanceEnables semantic search and document management using a local Qdrant vector database with OpenAI embeddings. Supports natural language queries, metadata filtering, and collection management for AI-powered document retrieval.9335MIT
- Flicense-qualityDmaintenanceEnables AI assistants to semantically search through indexed documentation websites and local code repositories using OpenAI embeddings and ChromaDB vector storage.
- Alicense-qualityDmaintenanceProvides token-efficient semantic search and document retrieval by indexing PDFs, text, and markdown files into local notebooks using ChromaDB. It enables AI agents to query relevant passages from large documents through local embedding models like Hugging Face or Ollama.1MIT
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