Stata Mcp

curl -fsSL https://raw.githubusercontent.com/SepineTam/mcp-for-stata/master/scripts/install.sh | bash

irm https://raw.githubusercontent.com/SepineTam/mcp-for-stata/master/scripts/install.ps1 | iex

As we find Claude Code is the best agent for MCP-for-Stata as its prefect agentic ability, we recommend using it, and there are lots of advanced usage following:

Generally, you can install MCP-for-Stata globally for one time, you can run:

claude mcp add stata-mcp --scope user -- uvx stata-mcp

Then, you do not need to watch it again.

If you want to install it locally only for the certain workspace, you cancdto your working directory, and run:

claude mcp add stata-mcp --env STATA_MCP__CWD=$(pwd) --scope local -- uvx --directory $(pwd) stata-mcp

It would nothing happen, you can typeclaudeand type/mcpto find the status.

Also, collaboration is an essential part of research. You can share your MCP config with your co-authors using:

claude mcp add stata-mcp --scope project -- uvx stata-mcp

In your working directory, you can find a file named.mcp.json, your mcp config will be placed here.

Then, you can use MCP-for-Stata in Claude Code. Here are some scenarios for using it:

- Paper Replication: Replicate empirical studies from economics papers
- Quick Hypothesis Testing: Validate economic hypotheses through regression analysis
- Stata Learning Assistant: Learn econometrics with step-by-step Stata explanations
- Code Organization: Review and optimize existing Stata do-files
- Result Interpretation: Understand complex statistical outputs and regression results

# Add the MCP-for-Stata marketplace claude plugin marketplace add SepineTam/mcp-for-stata # Install the plugin to local, project or user scope claude plugin install stata-toolbox -s project

The language server gives AI-generated Stata code better syntax awareness and completion, which improves output quality. We package the LSP in compliance with its license and give full attribution to the original author.

We find that many researchers are using Codex as their agent, therefore we also provide instructions for Codex users.

I figure that researchers are not using Codex CLI but Codex Desktop, so we can say it is easier to config MCP-for-Stata than other agents.

You just need to sayInstall MCP-for-Stata for yourself globally from https://www.statamcp.com or visit https://github.com/SepineTam/mcp-for-statathen restart your Codex Desktop after it say ready.

Also, if you want to install it manually, here are two ways:
- Open your Codex Desktop APP
- ClickSettingsin the bottom-left corner
- FindMCP serverson the left side
- ClickAdd server
- Fill with the following:

Name: stata-mcp Command to launch: uvx Arguments: stata-mcp

For CLI mode, just run the following command in your terminal

codex mcp add stata-mcp -- uvx stata-mcp

Claude Science runs MCP servers inside a strict sandbox that blocks access to the home directory (~) by default. If you try to launch MCP-for-Stata the standard way, you may see:

Standard config requires: please make sure the stata is installed at the default path, and the stata cli (for macOS and Linux) exists.

The standard config json as follows, you can DIY your config via add envs.

If you want to check whether your device is supported, you can run:

It displays basic information about your device and checks whether your setup is supported.

There are several Stata-related MCP projects. The table below was generated by Claude Code after analyzing each codebase directly.

If you use MCP-for-Stata in your research, and it really helps you, you can cite this repository using one of the following formats:

@software{sepinetam2025stata, author = {Song Tan}, title = {MCP-for-Stata: Integrate Stata into your agent}, year = {2025}, url = {https://github.com/sepinetam/mcp-for-stata}, version = {1.22.0} }

This is a web browser that enables your coding agent, such as Claude Code, to visit websites on your behalf and assist you in identifying bugs or creating UI test cases.

Query and analyze your Opik logs, traces, prompts and all other telemtry data from your LLMs in natural language.

Get access to Kaggle's datasets, models, competitions, notebook and benchmarks.

Interact with your MLOps and LLMOps pipelines through your ZenML MCP server

AI-powered document extraction infrastructure. Convert PDFs, Excel files, and scanned documents into structured JSON.

An MCP server for interacting with Dappier's Retrieval-Augmented Generation (RAG) models.

Solve optimization problems formulated by an LLM using the on-device Gurobi solver.