Research Methods Library

A Model Context Protocol server that grounds AI assistants in 17,777 sections of methodological texts on causal inference, econometrics, experimental design, marketing research, credit scoring, and artificial intelligence. Responses include study design recommendations, the assumptions to verify, runnable R and Python code, and a citation for every source. Access is free and requires no account or API key.

https://research-methods-mcp.vercel.app/mcp

Add to Cursor Install in VS Code

Installation

Claude (web, desktop, and mobile)

Settings → Connectors → Add custom connector; enter the endpoint URL above.

Claude Code

claude mcp add --transport http research-methods https://research-methods-mcp.vercel.app/mcp

ChatGPT

Enable developer mode in Settings, add a custom connector of type MCP server with the endpoint URL above, and select no authentication.

Cursor, Visual Studio Code, Windsurf, Cline, Zed, and other MCP clients

Use the buttons above, or add the following to the client's MCP configuration:

{
  "mcpServers": {
    "research-methods": {
      "url": "https://research-methods-mcp.vercel.app/mcp"
    }
  }
}

Example queries

Study design

"States raised the minimum wage in different years. How do I estimate the effect on teen employment?"

Implementation

"Give me Python code for a regression discontinuity with a data-driven bandwidth."

Identifying assumptions

"What could break my synthetic control estimate, and how do I test for it?"

Citation

"Write my methods section and give me BibTeX for the sources."

Tools

choose_method: Matches a research question to methods, with assumptions, R and Python packages, and readings.
search and fetch: Find and read sections, with code and links. Compatible with ChatGPT deep research.
get_code: Runnable R, Python, or Julia code for a task.
cite: References in APA, Chicago, MLA, Harvard, BibTeX, or RIS for the sections consulted.
list_sources: The works in the library: textbooks, an R package, and research articles.

Sources

A Guide on Data Analysis

Data analysis, econometrics, and causal inference in R: regression, GLMs, mixed models, experiments, difference-in-differences, synthetic control, regression discontinuity, instrumental variables, matching, double machine learning, mediation, sensitivity analysis, and structural IO.

Bash, Python, R · 1,611 sections

causalverse: The All-in-One Causal Inference Toolkit

R package for the full causal inference pipeline: RCTs and power, DiD and staggered adoption, synthetic DiD, synthetic control, RD, IV, event studies, matching, heterogeneous effects, mediation, sensitivity, and publication-ready output. Vignettes plus a function reference.

R · 1,163 sections

Credit Scoring: Theory, Methods, and Practice

Executable handbook on credit scoring in Python: scorecards, PD models, survival and time-to-default, reject inference, gradient boosting, deep learning, explainability, fairness, regulation (Basel, IFRS 9, CECL), and production deployment.

Bash, Python · 2,578 sections

Marketing Research: A Technical and Academic Treatment

Marketing research constructs, substantive domains (branding, advertising, pricing, CLV, influencer marketing, privacy), and methods (metrics, measurement, empirical and structural models) with reproducible R code.

Python, R · 1,820 sections

AI in Action: A Reference for Artificial Intelligence

Implementation-first reference for modern AI in Python: foundations, machine learning, deep learning, large language models, retrieval-augmented generation, and AI agents.

Bash, Julia, Python · 5,750 sections

Advanced Data Analysis

Prediction-focused companion to A Guide on Data Analysis, in R: machine learning, tree ensembles, regularization, deep learning, reinforcement learning, model evaluation and interpretation, MLOps, and applications of large language models.

Bash, Python, R · 1,844 sections

Applying Tidy Finance with Python to Vietnam

Empirical finance and asset pricing with Python on Vietnamese market data: data access and cleaning, returns, CAPM and factor models, portfolio sorts, Fama-MacBeth regressions, and fixed income.

Python · 1,334 sections

Tài Chính Ứng Dụng với Python

Vietnamese edition of Applying Tidy Finance with Python to Vietnam: empirical finance and asset pricing with Python on Vietnamese market data.

Python · 1,309 sections

Machine Learning in Python

Machine learning theory and industry applications in Python: supervised learning, regularization, optimization, matrix factorization, credit scoring and approval, fraud detection, text similarity, approximate nearest neighbors, multi-armed bandits, and two-sided matching.

Bash, Python · 361 sections

Research Articles and Publications

Peer-reviewed articles, Springer books, replication packages, and presentations by the author, from the ORCID record, with abstracts and DOIs.

· 7 sections