hex-mcp

## HexagonML ModelManager MCP Server This is a HexagonML MCP server that provides a Model context protocol interface for HexagonML ModelManager tools. ### Local Development #### Prerequisites - **Python**: `python3` - **Virtualenv**: recommended (project uses `.venv` in examples) #### Environment variables The server reads configuration from environment variables (and will also load a local `.env` file automatically). - **`SECRET_KEY`**: secret key for ModelManager API auth - **`MM_API_BASE_URL`**: base URL for ModelManager API (example: `http://localhost:8000`) - **`OUTPUT_DIR`**: directory where generated HTML outputs are written - **`HOST`** (optional): defaults to `0.0.0.0` - **`PORT`** (optional): defaults to `9000` Example `.env`: ```bash SECRET_KEY=your-secret-key MM_API_BASE_URL=http://localhost:8000 OUTPUT_DIR=./output HOST=0.0.0.0 PORT=9000 ``` #### Run the server From the repo root: ```bash python3 server/mm_mcp_server.py ``` To run with the FastMCP inspector (dev mode): ```bash fastmcp dev server/mm_mcp_server.py ``` #### Troubleshooting - **Port in use (FastMCP inspector)**: If you see `Proxy Server PORT IS IN USE` (commonly `6277`), stop the previous inspector process and retry. - **Missing env vars**: The server will exit with a message listing missing required variables. ### Configuration For mcp integration on host (g: windsurf, vscode, claude desktop) #### Local Configuration ```json { "mcpServers": { "hex-mm-mcp": { "command": "hex-mm-mcp/.venv/bin/mcp", "args": ["run", "hex-mm-mcp/server/mm_mcp_server.py"] } } } ``` #### Docker Configuration ##### For Dev (Using Local URL) - Run the ModelManager server in `--host 0.0.0.0 --port 8000` - cmd `python manage.py runserver 0.0.0.0:8000` - Get the hostname of your system using `hostname -I` command - eg: `192.168.10.75 172.17.0.1 2400:1a00:4b26:2af0:8f53:ede1:ec3a:c59b 2400:1a00:4b26:2af0:9139:c926:2fb5:6008` - use first ip address from the list eg: `192.168.10.75` - Replace `your-api-base-url` with `http://<hostIP>:8000` ```json { "mcpServers": { "hex-mm-mcp-docker": { "command": "docker", "args": [ "run", "--rm", "-i", "--network=host", "-e", "SECRET_KEY", "-e", "MM_API_BASE_URL", "-v", "OUTPUT_DIR", "<image-name>:<tag>" ], "env": { "SECRET_KEY": "your-secret-key", "MM_API_BASE_URL": "your-api-base-url", "OUTPUT_DIR": "your-output-dir" } } } } ``` ``` Docker Commands Build Image docker build --platform=linux/amd64 -t modelmanagerdev/mcp:version_id . Run Container docker run --platform=linux/amd64 -d --name mm-mcp -p 9000:9000 --env-file .env modelmanagerdev/mcp:v6 ``` ### Model Insights Tools The server exposes Model Insights endpoints via MCP tools. #### Create Insight - **Tool name**: `create_insight` - **Input**: `data` (dict) - **Backend**: `POST /api/mmanager-modelinsights/create_insight/` #### Get Insights - **Tool name**: `get_insight` - **Input**: `usecase_id` (str) - **Backend**: `GET /api/mmanager-modelinsights/get_insights/?usecase_id=...` - **Response shape**: - If the backend returns a dict, the tool returns that dict. - If the backend returns a list, the tool returns `{ "data": [...] }`.