mcp-automl 0.1.7

MCP server for end-to-end machine learning

MCP AutoML

MCP AutoML is a server that enables AI Agents to perform end-to-end machine learning workflows including data inspection, processing, model training. With MCP AutoML, AI Agents can perform more than a typical autoML framework. AI Agents can identify the target, setting baseline, and creating features by themselves.

MCP AutoML seperates tools and workflows, allowing you to create your own workflow.

Features

• Data Inspection: Analyze datasets with comprehensive statistics, data types, and previews
• SQL-based Data Processing: Transform and engineer features using DuckDB SQL queries
• AutoML Training: Train classification and regression models with automatic model comparison using PyCaret
• Prediction: Make predictions using trained models
• Multi-format Support: Works with CSV, Parquet, and JSON files

Usage

Configure MCP Server

Add to your MCP client configuration (e.g., Claude Desktop, Gemini CLI, Cursor, Antigravity):

{
  "mcpServers": {
    "mcp-automl": {
      "command": "uvx",
      "args": ["--python", "3.11", "mcp-automl"]
    }
  }
}

Or using Docker:

{
  "mcpServers": {
    "mcp-automl": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-v", "${PWD}:/workspace", "-v", "${HOME}/.mcp-automl:/root/.mcp-automl", "idea7766/mcp-automl:latest"]
    }
  }
}

Available Tools

Tool	Description
inspect_data	Get comprehensive statistics and preview of a dataset
query_data	Execute DuckDB SQL queries on data files
process_data	Transform data using SQL and save to a new file
train_classifier	Train a classification model with AutoML
train_regressor	Train a regression model with AutoML
predict	Make predictions using a trained model

Agent Skill

MCP AutoML includes an data science workflow skill that guides AI agents through best practices for machine learning projects. This skill teaches agents to:

• Identify targets and establish baselines
• Perform exploratory data analysis
• Engineer domain-specific features
• Train and evaluate models systematically

Installing the Skill

For Gemini CLI:

gemini skills install https://github.com/idea7766/mcp-automl --path skill/data-science-workflow

For Claude Code:

# Clone the repo and copy the skill
git clone https://github.com/idea7766/mcp-automl.git
cp -r mcp-automl/skill/data-science-workflow ~/.claude/skills/

The skill file is located at skill/data-science-workflow/SKILL.md.

Configuration

Models and experiments are saved to ~/.mcp-automl/experiments/ by default.

Troubleshooting

macOS: LightGBM OpenMP Error

If you encounter an error like Library not loaded: @rpath/libomp.dylib, you need to install OpenMP:

brew install libomp

This is a system-level dependency required by LightGBM on macOS. Linux and Windows users typically don't need this step.

Dependencies

• PyCaret - AutoML library
• DuckDB - Fast SQL analytics
• MCP - Model Context Protocol SDK