Research

Show mcp-stata identity, connected tools, and status. Use when the user asks if mcp-stata is available, asks about access to the toolkit, or asks what Stata tools are connected.

Build and review paper-ready regression, balance, and summary tables from Stata outputs. Use when the user needs a clean table for a draft, appendix, or coauthor share-out.

Install, configure, update, or verify mcp-stata across Claude Code, Codex, Gemini CLI, Cursor, Windsurf, and VS Code. Activate when users ask to set up the Stata toolkit or troubleshoot the installation.

Run arbitrary Stata code or a .do file and display the result.

Fetch and display stored r(), e(), and s() results from the last Stata command.

Run replication, robustness, and specification-sensitivity workflows for Stata projects. Use when a researcher wants to reproduce a result, rerun a pipeline, compare specifications, audit a do-file sequence, or check whether a claim is stable.

Organize and execute Stata workflows for referee responses, robustness requests, and coauthor follow-ups. Use when the user needs to answer a critique with targeted reruns, tables, figures, and a defensible audit trail.

Review regression outputs, tables, and graphs for publication readiness. Use when the user asks whether a result is ready for a paper, appendix, seminar, referee response, or coauthor review.

Plan and critique power, MDE, and sample-size calculations for Stata-based research workflows. Use when the user is designing a study, checking detectability, or defending precision claims.

Improve, modernize, and optimize existing Stata code for performance, portability, and maintainability. Use when legacy patterns such as preserve/restore, cd, #delimit, slow aggregation, or weak fixed-effects workflows appear in code under review.

Tail, read, or search a Stata log file from a previous command or background task.

Run static analysis on a Stata .do or .ado file and report style and best-practice issues.

Describe and summarize the current dataset in memory. Optionally inspect a specific variable with codebook.

Look up Stata command documentation and display formatted help text.

List, export, and review Stata graphs from the current session.

Diagnose local Stata, MCP, package, startup, graph-export, and permissions issues. Use when setup is failing, Stata is not discovered, packages are missing, logs are truncated, or a managed machine behaves differently from a normal workstation.

Track dataset lineage, transformation steps, merge logic, and reproducibility risks in Stata workflows. Use when the user needs to explain where data came from, how it changed, or why a pipeline can be trusted.

Audit datasets for structure, missingness, labeling, suspicious values, duplicate identifiers, and documentation readiness. Use when a researcher asks for data QA, codebook review, sanity checks, or pre-analysis cleanup guidance.

Design, run, and critique causal inference workflows in Stata. Use when the user is working on identification, treatment effects, DiD, IV, event studies, RD, or assumption-sensitive empirical claims.

Complete survival analysis library in Python. Handles right-censored data, Kaplan-Meier curves, and Cox regression. Standard for clinical trial analysis and epidemiology.

Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B...

Verify academic citations against CrossRef, Semantic Scholar, and OpenAlex. Detects AI-hallucinated references, chimeric citations, and suspicious patterns.

Create publication-quality figures that meet journal submission standards. APA 7th defaults with journal-specific overrides. Supports interaction plots, mediation path diagrams, forest plots, marginal effects, Johnson-Neyman plots, correlation heatmaps, and coefficient plots. All figures are colorblind-safe, high DPI, and exported in multiple formats (PDF, PNG, SVG, TIFF). Use when the user says "publication figures," "journal figures," "APA figures," "visualize results," "make plots," "inter...

Bidirectional review — THE entry point for every research engagement. Reviews everything the researcher has (data, docs, code, instruments) and produces two outputs: (1) a gap analysis showing what their project needs to meet gold standards, and (2) suite-learning findings identifying what our skill suite can learn from what they brought. Runs at the START of every engagement and in lighter form at session END. Use when the user says "I have data," "review what I have," "where do I start," "l...