ClearML MCP Server

A lightweight Model Context Protocol (MCP) server that enables AI assistants to interact with ClearML experiments, models, and projects. Get comprehensive ML experiment context and analysis directly in your AI conversations.

✨ Features

• 🔍 Experiment Discovery: Find and analyze ML experiments across projects
• 📊 Performance Analysis: Compare model metrics and training progress
• 📈 Real-time Metrics: Access training scalars, validation curves, and convergence analysis
• 🏷️ Smart Search: Filter tasks by name, tags, status, and custom queries
• 📦 Artifact Management: Retrieve model files, datasets, and experiment outputs
• 🌐 Cross-platform: Works with all major AI assistants and code editors

📋 Requirements

• uv (installation guide) for uvx command
• ClearML account with valid API credentials in ~/.clearml/clearml.conf

🚀 Quick Start# Prerequisites

You need a configured ClearML environment with your credentials in ~/.clearml/clearml.conf:

[api]
api_server = https://api.clear.ml
web_server = https://app.clear.ml
files_server = https://files.clear.ml
credentials {
    "access_key": "your-access-key",
    "secret_key": "your-secret-key"
}

Get your credentials from ClearML Settings.

Installation

pip install clearml-mcp

# Or run directly with uvx (no installation needed)
uvx clearml-mcp

🔌 Integrations

Add to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "clearml": {
      "command": "uvx",
      "args": ["clearml-mcp"]
    }
  }
}

Alternative with pip installation:

{
  "mcpServers": {
    "clearml": {
      "command": "python",
      "args": ["-m", "clearml_mcp.clearml_mcp"]
    }
  }
}

Add to your Cursor settings (Ctrl/Cmd + , → Search "MCP"):

{
  "mcp.servers": {
    "clearml": {
      "command": "uvx",
      "args": ["clearml-mcp"]
    }
  }
}

Or add to .cursorrules in your project:

When analyzing ML experiments or asking about model performance, use the clearml MCP server to access experiment data, metrics, and artifacts.

Add to your Continue configuration (~/.continue/config.json):

{
  "mcpServers": {
    "clearml": {
      "command": "uvx",
      "args": ["clearml-mcp"]
    }
  }
}

Add to your Cody settings:

{
  "cody.experimental.mcp": {
    "servers": {
      "clearml": {
        "command": "uvx",
        "args": ["clearml-mcp"]
      }
    }
  }
}

For any MCP-compatible AI assistant, use this configuration:

{
  "mcpServers": {
    "clearml": {
      "command": "uvx",
      "args": ["clearml-mcp"]
    }
  }
}

Compatible with:

• Zed Editor
• OpenHands
• Roo-Cline
• Any MCP-enabled application

🛠️ Available Tools

The ClearML MCP server provides 14 comprehensive tools for ML experiment analysis:

📊 Task Operations

• get_task_info - Get detailed task information, parameters, and status
• list_tasks - List tasks with advanced filtering (project, status, tags, user)
• get_task_parameters - Retrieve hyperparameters and configuration
• get_task_metrics - Access training metrics, scalars, and plots
• get_task_artifacts - Get artifacts, model files, and outputs

🤖 Model Operations

• get_model_info - Get model metadata and configuration details
• list_models - Browse available models with filtering
• get_model_artifacts - Access model files and download URLs

📁 Project Operations

• list_projects - Discover available ClearML projects
• get_project_stats - Get project statistics and task summaries
• find_project_by_pattern - Find projects matching name patterns
• find_experiment_in_project - Find specific experiments within projects

🔍 Analysis Tools

• compare_tasks - Compare multiple tasks by specific metrics
• search_tasks - Advanced search by name, tags, comments, and more

💡 Usage Examples# Demo

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

• "Show me the latest experiments in the 'computer-vision' project"
• "Compare the accuracy metrics between tasks task-123 and task-456"
• "What are the hyperparameters for the best performing model?"
• "Find all failed experiments from last week"
• "Get the training curves for my latest BERT fine-tuning"

🏗️ Development# Setup

git clone https://github.com/prassanna-ravishankar/clearml-mcp.git
cd clearml-mcp
uv sync

# Run locally
uv run python -m clearml_mcp.clearml_mcp

Available Commands

uv run task coverage

# Lint and format
uv run task lint
uv run task format

# Type checking
uv run task type

# Run examples
uv run task consolidated-debug  # Full ML debugging demo
uv run task example-simple      # Basic integration
uv run task find-experiments    # Discover real experiments

Testing with MCP Inspector

npx @modelcontextprotocol/inspector uvx clearml-mcp

🚨 Troubleshooting

"No ClearML projects accessible"

• Verify your ~/.clearml/clearml.conf credentials
• Test with: python -c "from clearml import Task; print(Task.get_projects())"
• Check network access to your ClearML server

Module not found errors

• Try bunx clearml-mcp instead of uvx clearml-mcp
• Or use direct Python: python -m clearml_mcp.clearml_mcp

Large dataset queries

• Use filters in list_tasks to limit results
• Specify project_name to narrow scope
• Use task_status filters (completed, running, failed)

Slow metric retrieval

• Request specific metrics instead of all metrics
• Use compare_tasks with metric names for focused analysis

🤝 Contributing

Contributions welcome! This project uses:

• UV for dependency management
• Ruff for linting and formatting
• Pytest for testing with 69% coverage
• GitHub Actions for CI/CD

See our testing philosophy and linting approach for development guidelines.

📄 License

MIT License - see LICENSE for details.

🔗 Links

• PyPI: clearml-mcp
• ClearML: clear.ml
• Model Context Protocol: MCP Specification

Created by Prass, The Nomadic Coder