Use LLMs with Stata to run and interpret regression analysis faster.
The available material is very limited, but based on known facts this is an open-source tool from an official registry with no clear high-risk red flags. The main concern is its inherent local code-execution capability and possible access to local analysis data, so it should be used in a constrained environment.
The material states that no keys or environment variables are required. There is no indication that API keys, account tokens, or other sensitive credentials are needed, so credential leakage and abuse risk appears low.
The material lists no remote endpoints and does not declare any need to connect to external services or send user data to third parties. Based on the available information, there is no clear data egress path.
The system flags executes-code, and its stated purpose—helping with Stata regression analysis—reasonably implies local invocation or control of Stata or related processes. This is a standard MCP capability, but local process execution still warrants attention to command and resource boundaries.
A regression-analysis tool would typically need to read local datasets and may generate output files. Although the material does not specify path scope and shows no obvious overbroad authorization, its function implies access to user analysis data and output files, so visible directories should be minimized.
Positive signals include an official registry listing, an auditable open-source repository, and updates within the last year. However, the missing README, undeclared license, and very low community adoption (0 stars) limit evidence of maturity and auditability. This supports a caution rating rather than high risk.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.SepineTam/stata-mcp" MCP server from askskill: Run: claude mcp add 'io-github-sepinetam-stata-mcp' -- npx -y stata-mcp
Manage, edit, and run Stata do files using natural language.
Run statistical analysis, probability calculations, and data processing through natural language.
Run reproducible econometrics and statistical workflows through headless R execution.
Search, explore, and retrieve ISTAT statistical data using natural language.
Load datasets, compute statistics, and create charts for data exploration.
Quickly compute data metrics and descriptive statistics with clear analytical outputs.