MLflow MCP Server

A Model Context Protocol (MCP) server that enables LLMs to interact with MLflow tracking servers. Query experiments, analyze runs, compare metrics, and explore the model registry - all through natural language.

Features

Experiment Management: List and search experiments, discover available metrics and parameters
Run Analysis: Retrieve run details, query runs with filters, find best performing models
Metrics & Parameters: Get metric histories, compare parameters across runs
Artifacts: Browse and download run artifacts
Model Registry: Access registered models, versions, and deployment stages
Comparison Tools: Side-by-side run comparisons, best run selection
Tag-based Search: Filter runs by custom tags
Pagination: Offset-based pagination for browsing large result sets

Installation

Using uvx (Recommended)

bash

# Run directly without installation
uvx mlflow-mcp

# Or install globally
pip install mlflow-mcp

From Source

bash

git clone https://github.com/kkruglik/mlflow-mcp.git
cd mlflow-mcp
uv sync
uv run mlflow-mcp

Configuration

Claude Desktop

Add to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

Linux: ~/.config/claude/claude_desktop_config.json

json

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

Environment Variables

**MLFLOW_TRACKING_URI** (required): Your MLflow tracking server URL
Examples: http://localhost:5000, https://mlflow.company.com

Available Tools

Experiments

**get_experiments()** - List all experiments
**get_experiment_by_name(name)** - Get experiment by name
**get_experiment_metrics(experiment_id)** - Discover all unique metrics
**get_experiment_params(experiment_id)** - Discover all unique parameters

Runs

**get_runs(experiment_id, limit=3, offset=0, order_by=None)** - Get runs with full details. Supports sorting and pagination
**get_run(run_id)** - Get detailed run information for a specific run
**query_runs(experiment_id, query, limit=3, offset=0, order_by=None)** - Filter and sort runs (e.g., "metrics.accuracy > 0.9", order_by="metrics.accuracy DESC")
**search_runs_by_tags(experiment_id, tags, limit=3, offset=0)** - Find runs by tags with pagination

Metrics & Parameters

**get_run_metrics(run_id)** - Get all metrics for a run
**get_run_metric(run_id, metric_name)** - Get full metric history with steps

Artifacts

**get_run_artifacts(run_id, path="")** - List artifacts (supports browsing directories)
**get_run_artifact(run_id, artifact_path)** - Download artifact
**get_artifact_content(run_id, artifact_path)** - Read artifact content (text/json)

Analysis & Comparison

**get_best_run(experiment_id, metric, ascending=False)** - Find best run by metric (supports special characters)
**compare_runs(experiment_id, run_ids)** - Side-by-side comparison with full data

Model Registry

**get_registered_models()** - List all registered models
**get_model_versions(model_name)** - Get all versions of a model
**get_model_version(model_name, version)** - Get version details with metrics

Health

**health()** - Check server connectivity

Usage Examples

Ask Claude

"Show me all experiments in MLflow"

"What are the top 5 runs by accuracy in experiment 'my-experiment'?"

"Compare runs abc123 and def456"

"Which model has the highest F1 score?"

"Show me the training loss curve for run xyz789"

"List all production models in the registry"

Requirements

Python >=3.10
MLflow >=3.4.0
Access to an MLflow tracking server

License

MIT License - see LICENSE file for details.

Contributing

Contributions welcome! Please open an issue or submit a pull request.

Mlflow Mcp

Documentation