How do I use HexagonML ModelManager MCP Server?

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., "@HexagonML ModelManager MCP Server list all my deployed models" 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.

HexagonML ModelManager MCP Server by hexsupport

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:

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:

python3 server/mm_mcp_server.py

To run with the FastMCP inspector (dev mode):

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

{ "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

{ "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": [...] }.