MLflow MCP Server

A Model Context Protocol (MCP) server that exposes MLflow experiment tracking and model registry operations as tools for AI assistants.

Table of Contents

• Quickstart
• Tools
• Example Prompts
• Configuration
• Installation (Development)
• Project Structure
• License

Tools

Experiment Management

Run Management

Model Registry

Example Prompts

Once configured, you can ask your AI assistant things like:

Exploring experiments and runs:

• "List all experiments related to recommendation models"
• "Show me the runs for experiment 12 and compare their metrics"
• "Get the parameters and metrics for run abc123"
• "Which runs in the fraud-detection experiment have the highest accuracy?"

Managing the model registry:

• "Show me all registered models"
• "Register a new model called churn-classifier with description 'Binary classifier for customer churn'"
• "Create a new version of churn-classifier from run abc123"
• "Set the champion alias on version 3 of churn-classifier"
• "Rename the model old-name to new-name"
• "Delete version 1 of churn-classifier"

Analysis and comparison:

• "Compare the last 5 runs of the search-ranking experiment by NDCG and latency"
• "What hyperparameters were used in the best-performing run of experiment 7?"
• "List all model versions for recommendation-model and their aliases"

Project Structure

mlflow_mcp_server/
├── __main__.py              # Entry point
├── server.py                # MCP server setup and tool registration
├── tools/
│   ├── experiment_tools.py  # Experiment search and retrieval
│   ├── run_tools.py         # Run details and listing
│   └── registered_models.py # Model registry CRUD operations
└── utils/
    └── mlflow_client.py     # MLflow client singleton

Adding New Tools

1. Create a function in the appropriate file under tools/.
2. Register it in server.py:

from mlflow_mcp_server.tools.your_module import your_function
mcp.add_tool(your_function)

Linting

uv run ruff check .
uv run ruff format --check .

Quickstart

The fastest way to get started is to add the server to your MCP client config. No local clone required.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "mlflow": {
      "command": "uvx",
      "args": ["mlflow-mcp-server"],
      "env": {
        "MLFLOW_TRACKING_URI": "http://localhost:5000"
      }
    }
  }
}

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "mlflow": {
      "command": "uvx",
      "args": ["mlflow-mcp-server"],
      "env": {
        "MLFLOW_TRACKING_URI": "http://localhost:5000"
      }
    }
  }
}

OpenCode

Add to your opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "mlflow": {
      "type": "local",
      "command": ["uvx", "mlflow-mcp-server"],
      "environment": {
        "MLFLOW_TRACKING_URI": "http://localhost:5000"
      }
    }
  }
}

Replace http://localhost:5000 with the URL of your MLflow tracking server.

Configuration

Installation (Development)

Prerequisites

• Python 3.11+
• uv
• An MLflow tracking server

Setup

git clone https://github.com/yesid-lopez/mlflow-mcp-server.git
cd mlflow-mcp-server
uv sync

Running Locally

export MLFLOW_TRACKING_URI="http://localhost:5000"
uv run -m mlflow_mcp_server

The server communicates over stdio, which is the standard MCP transport for local tool servers.

License

MIT