by tmonk · MCP Server · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Stata MCP Server (mcp-stata) mcp-stata is an agentic toolkit for empirical researchers. It gives AI agents native control over a local Stata installation, allowing agents to run do-files, inspect data, check stored results, and export graphs. Contains a skills catalog for workflows researchers use often: auditing data, replication and robustness checks, specification comparisons, publication QA, referee responses, and modernization of legacy code. Featured in News. If you'd like a fully integrated VS Code extension to run Stata code without leaving your IDE, and also allow AI agent interaction, check out my other project: [ Stata Workbench](https://github.com/tm
| Stars | 60 |
| Forks | 11 |
| Language | Python |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 47.15/100 |
| Open Issues | 5 |
| Last Updated | 2026-05-11 |
| Created | 2025-12-12 |
| Platforms | mcp, python |
| Est. Tokens | ~150k |
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Looking for a mcp-stata alternative? If you're comparing mcp-stata with other mcp server tools, these 5 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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mcp-stata is A lightweight Model Context Protocol (MCP) server for Stata. Execute commands, inspect data, retrieve stored results (r()/e()), and view graphs in your chat interface. Built for economists who want to. It is categorized as a MCP Server with 60 GitHub stars.
mcp-stata is primarily written in Python. It covers topics such as ai, econometrics, economics.
You can find installation instructions and usage details in the mcp-stata GitHub repository at github.com/tmonk/mcp-stata. The project has 60 stars and 11 forks, indicating an active community.
mcp-stata is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to mcp-stata on Agent Skills Hub include StatsPAI, mcp-for-stata, stata-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.