SepineTam/mcp-for-stata

Description and use

**Summary**
SepineTam’s **mcp‑for‑stata** is a Python‑based implementation of the Model Context Protocol (MCP) that lets AI agents invoke Stata commands and retrieve results through a clean, standardised API. By exposing Stata as a first‑class tool in the MCP ecosystem, it enables developers to plug large‑language‑model assistants into statistical workflows without writing custom wrappers.

**Value**

• **Standardised integration** – MCP provides a common contract for tool‑calling, so the same agent code can interact with Stata, Python, or any other MCP‑enabled service.
• **Rapid prototyping** – Developers can prototype AI‑driven analytics pipelines by simply calling Stata functions as if they were remote procedures, cutting down on glue code.
• **Reusability** – Once the MCP server is running, any MCP‑compatible assistant (e.g., LangChain, Auto‑GPT, or custom LLM agents) can reuse the Stata integration without additional adapters.

**Practical adoption path**

1. **Clone & install** – Pull the repository, set up the Python environment (pip install -r requirements.txt), and configure the Stata executable path.
2. **Run the MCP server** – Launch the provided CLI (python -m mcp_for_stata serve) to start a local HTTP/WS server exposing Stata commands.
3. **Connect your agent** – In your LLM‑agent code, point the MCP client to the server URL and use the documented method signatures (e.g., run("regress y x1 x2")).
4. **Extend & secure** – Add custom Stata macros or wrap additional Stata packages, and place the server behind authentication or a reverse proxy for production use.

**Production readiness**

• **Activity & adoption** – 206 ★, 30 forks, last commit 2026‑06‑28; the repo shows ongoing maintenance and community interest.
• **Maturity** – Implements the full MCP spec, provides API/SDK/CLI entry points, and includes basic health‑check endpoints, making it suitable for pilot deployments.
• **Risk considerations** – No immediate licensing or security red flags, but a final review of the open‑source license, dependency vulnerabilities, and maintainer responsiveness is recommended before full‑scale rollout.

Overall, **mcp‑for‑stata** offers a high‑signal, low‑friction way to bring Stata into LLM‑driven applications, and it is mature enough for serious pilot projects with only standard OSS due‑diligence steps remaining.

Use in a workflow

SepineTam/mcp-for-stata helps connect AI assistants to real tools and data through a standard protocol.

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Limitations and review notes

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.