MLOps MCP Server

AI-powered MLOps workflows through Claude Code

An MCP (Model Context Protocol) server that enables Claude to interact with ML experiment tracking, model registries, and deployment pipelines across popular MLOps platforms.

Features

• MLflow Integration - List experiments, compare runs, find best models, search with filters
• Model Registry - Browse registered models, track versions, check deployment stages
• Cross-Platform - Unified interface for MLflow, Weights & Biases, and SageMaker (coming soon)

Quick Start

Installation

# Install from PyPI
pip install mlops-mcp-server

# Or install with all optional dependencies
pip install mlops-mcp-server[all]

Configuration

Add to your Claude Code MCP configuration (~/.claude.json):

{
  "mcpServers": {
    "mlops": {
      "command": "mlops-mcp-server",
      "env": {
        "MLFLOW_TRACKING_URI": "http://localhost:5000"
      }
    }
  }
}

Environment Variables

Variable	Description	Default
MLFLOW_TRACKING_URI	MLflow tracking server URI	./mlruns
WANDB_API_KEY	Weights & Biases API key	-
AWS_REGION	AWS region for SageMaker	us-east-1

Available Tools

Experiment Tracking

Tool	Description
mlflow_list_experiments	List all MLflow experiments
mlflow_get_runs	Get runs for an experiment with metrics
mlflow_compare_runs	Compare metrics across multiple runs
mlflow_get_best_run	Find best run by metric
mlflow_search_runs	Search runs with SQL-like filters

Model Registry

Tool	Description
mlflow_list_models	List registered models
mlflow_get_model_versions	Get model version history

Usage Examples

List Experiments

User: Show me all my MLflow experiments

Claude: [Uses mlflow_list_experiments]
Found 5 experiments:
1. fraud-detection (ID: 1) - 23 runs
2. recommendation-engine (ID: 2) - 45 runs
...

Find Best Model

User: Which model has the highest accuracy in the fraud-detection experiment?

Claude: [Uses mlflow_get_best_run]
Best run: run_abc123
- Accuracy: 0.956
- Model: XGBoost
- Parameters: max_depth=6, learning_rate=0.1

Compare Runs

User: Compare the last 3 runs in terms of accuracy and F1 score

Claude: [Uses mlflow_compare_runs]
| Run ID | Accuracy | F1 Score |
|--------|----------|----------|
| abc123 | 0.956    | 0.943    |
| def456 | 0.948    | 0.935    |
| ghi789 | 0.951    | 0.940    |

Development

Setup

# Clone the repository
git clone https://github.com/elliottdevo8/mlops-mcp-server.git
cd mlops-mcp-server

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install in development mode
pip install -e ".[dev]"

Running Tests

pytest tests/ -v

Running Locally

# Start the server
python -m mlops_mcp.server

# Or use the CLI entry point
mlops-mcp-server

Roadmap

• [x] MLflow experiment tracking
• [x] MLflow model registry
• [ ] Weights & Biases integration
• [ ] SageMaker model registry
• [ ] SageMaker endpoint management
• [ ] Model drift monitoring
• [ ] Cost analysis tools

Contributing

Contributions are welcome! Please read our Contributing Guide for details.

License

MIT License - see LICENSE for details.

Acknowledgments

Built with the Model Context Protocol by Anthropic.