It is a Model Context Protocol (MCP) server that allows Claude to interact directly with Stata to execute code, inspect data, and retrieve results.

Can I view Stata graphs in Claude?

Yes, the skill supports the visualization of Stata-generated graphs directly within your chat interface.

Is this suitable for professional econometric research?

Absolutely. It was specifically built for economists to integrate LLM capabilities into rigorous statistical workflows.

Does it support stored results like e(sample) or r(mean)?

Yes, one of its core features is the ability to retrieve and display stored results (r-class and e-class) for detailed interpretation.

Do I need Stata installed to use this skill?

Yes, this skill requires a local installation of Stata as it acts as a bridge between the LLM and your local Stata environment.

Stata Integration for Claude

Name: Stata Integration for Claude
Author: tmonk

bytmonk

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Data Science & ML

Executes Stata commands and manages statistical workflows directly through the Claude interface.

About

The mcp-stata skill bridges the gap between Large Language Models and Stata, allowing researchers and economists to perform complex statistical analysis directly through chat. It enables the execution of commands, data inspection, retrieval of stored results like r-class and e-class scalars, and the visualization of generated graphs. This integration streamlines the econometrics workflow by providing AI-powered assistance for debugging Stata code, interpreting outputs, and automating repetitive data cleaning tasks without leaving the development environment.

Key Features

Execute Stata commands and .do files in real-time
Inspect datasets, variable properties, and observations
Retrieve and analyze stored results (r() and e() values)
4 GitHub stars
View and manage Stata-generated graphs and visualizations
Automated debugging and optimization of econometric code

Use Cases

Interpreting regression results and statistical outputs with AI-driven insights
Automating complex data cleaning and merging processes for large datasets
Rapidly prototyping and testing econometric models and simulations

About

Key Features

Execute Stata commands and .do files in real-time
Inspect datasets, variable properties, and observations
Retrieve and analyze stored results (r() and e() values)
4 GitHub stars
View and manage Stata-generated graphs and visualizations
Automated debugging and optimization of econometric code

Use Cases

Interpreting regression results and statistical outputs with AI-driven insights
Automating complex data cleaning and merging processes for large datasets
Rapidly prototyping and testing econometric models and simulations