mlflow-mcp 0.2.1

A Model Context Protocol (MCP) server for MLflow - enables LLMs to interact with MLflow experiments, runs, metrics, and models

MLflow MCP Server

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

Features

• Experiment Management: List, search, and filter experiments
• Run Analysis: Query runs, compare metrics, find best performing models
• Metrics & Parameters: Get metric histories, compare parameters across runs
• Artifacts: Browse and download run artifacts
• LoggedModel Support: Search and retrieve MLflow 3 LoggedModel entities
• Model Registry: Full registry management — register, tag, alias, stage, and promote models
• Write Actions: Tag runs/experiments, assign aliases, register and promote models
• MCP Prompts: Built-in guided workflows for common tasks
• Pagination: Offset-based pagination for browsing large result sets

Installation

Using uvx (Recommended)

# Run directly without installation
uvx mlflow-mcp

# Or install globally
pip install mlflow-mcp

From Source

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

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

Claude Code (project-scoped)

Add .mcp.json to your project root:

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

Authenticated Server

For MLflow servers with authentication, add credentials to the env block:

{
  "mcpServers": {
    "mlflow": {
      "command": "uvx",
      "args": ["mlflow-mcp"],
      "env": {
        "MLFLOW_TRACKING_URI": "https://mlflow.company.com",
        "MLFLOW_TRACKING_USERNAME": "your-username",
        "MLFLOW_TRACKING_PASSWORD": "your-password"
      }
    }
  }
}

For Databricks or token-based auth, use MLFLOW_TRACKING_TOKEN instead:

{
  "mcpServers": {
    "mlflow": {
      "command": "uvx",
      "args": ["mlflow-mcp"],
      "env": {
        "MLFLOW_TRACKING_URI": "https://mlflow.company.com",
        "MLFLOW_TRACKING_TOKEN": "your-token"
      }
    }
  }
}

Environment Variables

Required:

• MLFLOW_TRACKING_URI — Your MLflow tracking server URL
  • Examples: http://127.0.0.1:5000, https://mlflow.company.com

Authentication (if your server requires it):

• MLFLOW_TRACKING_USERNAME + MLFLOW_TRACKING_PASSWORD — HTTP Basic Auth (MLflow built-in auth plugin)
• MLFLOW_TRACKING_TOKEN — Bearer token auth (Databricks or token-based setups)

Available Tools

Experiments

• get_experiments() - List all experiments
• search_experiments(filter_string, order_by, max_results) - Filter and sort 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
• set_experiment_tag(experiment_id, key, value) - Tag an experiment

Runs

• get_runs(experiment_id, limit=3, offset=0, order_by=None) - Get runs with full details
• get_run(run_id) - Get detailed run information
• get_parent_run(run_id) - Get parent run for nested runs
• query_runs(experiment_id, query, limit=3, offset=0, order_by=None) - Filter and sort runs
• search_runs_by_tags(experiment_id, tags, limit=3, offset=0) - Find runs by tags
• set_run_tag(run_id, key, value) - Tag a run

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
• compare_runs(experiment_id, run_ids) - Side-by-side run comparison

Logged Models (MLflow 3)

• search_logged_models(experiment_ids, filter_string, order_by, max_results) - Search logged models by metrics/params/tags
• get_logged_model(model_id) - Get full details of a logged model

Model Registry

• get_registered_models() - List all registered models
• get_registered_model(name) - Full model details including versions and aliases
• get_model_versions(model_name) - Get all versions of a model
• get_model_version(model_name, version) - Get version details with metrics
• get_model_version_by_alias(name, alias) - Get version by alias (e.g. "champion")
• get_latest_versions(name, stages) - Get latest versions per stage
• register_model(model_name, model_uri, tags) - Register a model into the registry
• update_model_version(name, version, description) - Update version description
• set_registered_model_tag(name, key, value) - Tag a registered model
• set_model_alias(name, alias, version) - Assign an alias to a model version
• transition_model_version_stage(name, version, stage) - Transition to Staging/Production/Archived
• copy_model_version(src_model_name, src_version, dst_model_name) - Promote version to another registered model

Health

• health() - Check server connectivity

Prompts

Built-in guided workflows available as slash commands:

• compare_runs_by_ids - Compare specific runs side-by-side
• find_best_run - Find and analyze the best run in an experiment by metric
• promote_best_model - End-to-end: find best model → register → tag → alias → promote

Usage Examples

Explore experiments and runs

"Show me all experiments. Which ones were updated recently?"

"What metrics and parameters are tracked in experiment 'fraud-detection'?"

"Get the top 10 runs in 'fraud-detection' sorted by test/f1. Show me the params that differ most between the top 3."

"Find all runs tagged with model_type=lightgbm and compare their recall scores."

Analyze a training run

"Show me the full details of run abc123 — metrics, params, and artifacts."

"Plot the training loss curve for run abc123." (Claude fetches metric history and renders a chart)

"This run has a parent — show me the parent run and compare their metrics."

Find and register the best model

"Find the best logged model in experiment 'fraud-detection' by test/recall. Register it as 'fraud-classifier' with a selection_metric tag."

"Which logged model in experiments 1 and 2 has the highest F1 score on the validation set?"

"Register the model from run abc123 artifact path 'model/' as 'my-classifier'."

Manage the model registry

"Show me all versions of 'fraud-classifier' with their aliases and stages."

"Set the champion alias on version 3 of fraud-classifier."

"Update the description of fraud-classifier v3 to explain what dataset it was trained on."

"Copy fraud-classifier v3 to a separate 'fraud-classifier-prod' model as the production entry."

End-to-end promotion workflow

"Find the best model in 'fraud-detection' by test/recall, register it as 'fraud-classifier', tag it with the framework and problem type, and set it as champion. Ask me before copying to prod."

(This maps directly to the promote_best_model built-in prompt)

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.

Links

• PyPI Package
• GitHub Repository
• MLflow Documentation
• Model Context Protocol