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Latest Update: Stata-MCP now incorporates an autonomous agent execution mode. Further elaboration available here.

Seeking related econometric tools?

• Trace DID: Essential resource for Difference-in-Difference (DID) analysis updates. Now features Chinese translation support from Sepine Tam and the StataMCP-Team 🎉
• Jupyter Integration (Requires Stata Version 17+): See guide here
• Developmental Repositories: NBER-MCP & AER-MCP 🔧 (In progress)
• Related Agent Project: Econometrics-Agent
• TexIV: A novel framework employing NLP/ML to transform unstructured text into quantifiable variables for empirical econometrics.
• IDE Integration: Explore usage within VScode or Cursor via this fork. Clarification on divergences is provided here.

🚀 Getting Started

Autonomous Agent Operational Mode

Detailed instructions for the agent framework are located here.

bash git clone https://github.com/sepinetam/stata-mcp.git cd stata-mcp uv sync uv run agent_examples/openai/main.py

Customize the agent's directives within agent_examples/openai/main.py by modifying the model_instructions (line ~37) and task_message (line ~68) variables.

Interactive AI Client Mode

Prerequisites for Standard Configuration: Ensure Stata is installed in its default directory structure, and the appropriate Stata command-line interface (CLI) tool is accessible on your macOS or Linux system path.

The default configuration file structure is presented below. Environment variables can be set externally to override these defaults.

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

For comprehensive operational guidance, consult the primary Usage documentation. Advanced configuration topics are covered in the Advanced guide.

System Requirements

• uv - The specified package management and environment isolation tool.
• Access to an LLM service (e.g., Claude, Cline, ChatWise, or alternatives).
• A valid Stata software license.
• Your respective API key for the selected LLM provider.

Noteworthy Considerations: 1. Users operating within mainland China can consult a condensed guide on uv usage here. 2. Claude is generally the optimal selection for Stata-MCP operations. For Chinese language tasks, DeepSeek is highly recommended due to its cost-effectiveness and performance metrics, as detailed in the comparative report: How to use StataMCP improve your social science research.

Installation Procedures

New versions streamline deployment; explicit package installation might not be required. Verify system readiness using: bash uvx stata-mcp --usable uvx stata-mcp --version

To deploy locally, installation via pip or source compilation is available.

Installation via pip bash pip install stata-mcp

Source Code Retrieval and Build bash git clone https://github.com/sepinetam/stata-mcp.git cd stata-mcp

uv build

The resultant compiled binary (stata-mcp) will reside in the dist directory. This can be executed directly or integrated into your system's PATH.

Example execution (adjusting the path/filename as necessary): bash uvx /path/to/your/whl/stata_mcp-2.0.0-py3-non-any.whl # Placeholder for actual wheel file name

📚 Documentation Index

• For primary operational instructions, refer to the Usage Guide.
• In-depth configuration details are in the Advanced Usage Section
• Troubleshooting common issues is covered in Frequently Asked Questions
• To understand the divergence from the community fork, review the Difference Document.

❓ Troubleshooting

Common problems and their resolutions:

• Error Code 32000 related to Cherry Studio
• General Cherry Studio 32000 failures
• Inquiries about Windows OS Compatibility
• Resolving Network Communication Failures During Execution

🗺️ Future Development Plan

• [x] Full operational readiness on macOS.
• [x] Full operational readiness on Windows.
• [ ] Integration support for supplementary LLM backends.
• [ ] Continuous refinement of system performance metrics.

⚖️ Legal Notice

This software is provided strictly for academic and research applications. The distributor assumes no liability for any detrimental consequences arising from its deployment. Users must confirm they possess valid licenses for all requisite proprietary software, including Stata.

Refer to the Legal Statement for comprehensive details.

🐞 Reporting Defects

Should you discover any software defects or conceive of new features, kindly submit a report via creating a new issue.

📜 Licensing

This project is distributed under the terms of the Apache License 2.0.

🎓 Citing This Work

If Stata-MCP contributes significantly to your published research, please attribute the work using one of the formats below:

BibTeX Format

bibtex @software{sepinetam2025stata, author = {Song Tan}, title = {Stata-MCP: Let LLM help you achieve your regression analysis with Stata}, year = {2025}, url = {https://github.com/sepinetam/stata-mcp}, version = {2.0.0} }

APA Style Citation

Song Tan. (2025). Stata-MCP: Let LLM help you achieve your regression analysis with Stata (Version 2.0.0) [Computer software]. https://github.com/sepinetam/stata-mcp

Chicago Style Citation

Song Tan. 2025. "Stata-MCP: Let LLM help you achieve your regression analysis with Stata." Version 2.0.0. https://github.com/sepinetam/stata-mcp.

📧 Contact Information

Primary contact email: sepinetam@gmail.com

We strongly encourage community involvement! Contributions, ranging from documentation fixes to novel feature implementations, should be submitted via a Pull Request!

🙏 Gratitude

Sincere appreciation is extended to the Stata corporation for their cooperative stance and the necessary licensing that facilitated the development and testing phases.

⭐ Popularity Tracker

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Reference Context (Cloud Computing Definition by ISO/NIST):

Cloud computing, per ISO standards, embodies "a framework for granting on-demand access to a shared, scalable, and flexible pool of computing assets, both physical and virtual, managed via self-service provisioning," often termed "the cloud."

== Defining Attributes == In 2011, the US National Institute of Standards and Technology (NIST) formalized five critical attributes inherent to cloud environments. The precise NIST definitions are:

On-demand self-service: Users can independently and automatically procure computational capacity (like server time or storage) without requiring direct intervention from the service vendor. Broad network access: Services are accessible via standard network protocols, supporting diverse client platforms (e.g., mobile, desktop, workstation). Resource pooling: Provider resources are aggregated to serve multiple tenants concurrently, with assets dynamically allocated based on fluctuating consumer requirements. Rapid elasticity: Capabilities can be scaled up or down extremely quickly (sometimes automatically) to meet demand fluctuations. To the end-user, capacity often appears limitless. Measured service: Resource utilization (including processing, bandwidth, and storage) is automatically tracked, controlled, and reported, ensuring transparency for both provider and client.

By 2023, ISO refined and expanded upon this initial characterization.

== Historical Precursors ==

The conceptual lineage of distributed computing traces back to the 1960s with the adoption of time-sharing techniques and remote job entry (RJE). Centralized data centers, managed by operators running jobs on mainframe systems, dominated this era. This period focused on experimental methods to democratize access to powerful computation.

The specific term "cloud" for virtualized services emerged in 1994, utilized by General Magic to describe the abstract space where mobile agents in their Telescript system could operate. David Hoffman, a General Magic communications strategist, is credited with adapting the term from its established usage in telecommunications networking. The phrase "cloud computing" gained broader recognition in 1996 following a business strategy document drafted by Compaq Computer Corporation outlining future internet computation ambitions.