mcp-for-stata: Stata-MCP: A MCP server for integrating Stata into your agent loop with a safety-first design

by SepineTam · MCP Server · ★ 275

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 73/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

claude-codeclaude-code-pluginclaude-code-skillcodexcodex-plugindsh-plugindsh-pluginseconometricsempiricalempirical-research

Quick Facts

Stars275
Forks36
LanguagePython
CategoryMCP Server
LicenseAGPL-3.0
Quality Score73/100
Open Issues2
Last Updated2026-11-06
Created2025-03-28
Platformsclaude-code, codex, mcp, python
Est. Tokens~17k

About mcp-for-stata

MCP-for-Stata: Integrate Stata into your agent Stata is a registered trademark of StataCorp LLC. This project is an independent community-developed tool and is not affiliated with, endorsed by, or sponsored by StataCorp LLC. <!-- mcp-name: io.github.SepineTam/mcp-for-

Compatible Skills

These tools work well together with mcp-for-stata for enhanced workflows:

  • CoBRA — semantic(0.51)+complementary+rare_topics+same_lang+similar_pop+shared_platform (67%)
  • edsl — semantic(0.42)+complementary+rare_topics+same_lang+similar_pop+shared_platform (64%)
  • science-skills — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (55%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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