MLflow MCP Server

Integrates with MLflow, enabling AI assistants to interact with experiments, runs, and registered models.

GitHub

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

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.

Tools

Experiment Management

ToolDescription
get_experimentGet experiment details by ID
get_experiment_by_nameGet experiment details by name
search_experimentsList and filter experiments with optional name matching and pagination

Run Management

ToolDescription
get_runGet full run details including metrics, parameters, tags, and run type (parent/child/standalone)
get_experiment_runsList runs for an experiment with pagination

Model Registry

ToolDescription
get_registered_modelsSearch and list registered models
get_model_versionsBrowse model versions with filtering
create_registered_modelCreate a new registered model with optional description and tags
create_model_versionCreate a new model version from a run's artifacts
rename_registered_modelRename an existing registered model
set_registered_model_aliasAssign an alias (e.g. champion, challenger) to a model version
delete_registered_modelDelete a registered model and all its versions
delete_model_versionDelete a specific model version

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"

Configuration

Environment VariableDefaultDescription
MLFLOW_TRACKING_URIhttp://localhost:5000URL of the MLflow tracking server

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.

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 .

License

MIT

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