Research Methods Library

A Model Context Protocol server that grounds AI assistants in methodological texts: study design recommendations, identifying assumptions, runnable R and Python code, and a citation for every source.

The Research Methods Library is a Model Context Protocol (MCP) server. Given a research question, it recommends candidate designs, states their identifying assumptions, returns runnable R and Python code, and supplies a formatted citation for every source consulted. Its index contains approximately 13,000 sections from four textbooks and one R package (listed under Sources) covering causal inference, econometrics, experimental design, marketing research, credit scoring, machine learning, and artificial intelligence.

The server works with Claude, ChatGPT, Cursor, Visual Studio Code, and any other MCP-compatible client. Access is free and requires no account or API key.

Installation

Endpoint: https://mcp.research-methods.deno.net/mcp. Access is free and requires no account or API key.

Client	Configuration
Claude (web, desktop, and mobile)	Settings → Connectors → Add custom connector; enter the endpoint
Claude Code	claude mcp add --transport http research-methods https://mcp.research-methods.deno.net/mcp
ChatGPT	Settings → enable developer mode → add a custom connector of type MCP server with the endpoint and no authentication
Cursor, Visual Studio Code	Use the buttons above
Windsurf, Cline, Zed, and other clients	Add the following JSON to the client's MCP configuration

{
  "mcpServers": {
    "research-methods": {
      "url": "https://mcp.research-methods.deno.net/mcp"
    }
  }
}

Example queries

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

For this query the assistant calls choose_method, which returns staggered difference-in-differences together with its scope conditions, the reason a conventional two-way fixed-effects regression can be biased when treatment effects are heterogeneous, the assumptions to verify, implementations in R (did, fixest, didimputation, HonestDiD) and Python (csdid, pyfixest, differences), and the relevant readings.

Give me Python code for a probability-of-default model with gradient boosting.

Only California passed the law and I have 30 years of state data. What can I do?

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

Each response links to the specific section consulted and carries the citation of the work from which it is drawn.

Tools

Tool	What it does
choose_method	Matches a research question to methods, with when to use each, key assumptions, R and Python packages, and readings. 62 curated methods.
search	Finds sections across all works, with links, snippets, code languages, and a citation. Compatible with ChatGPT deep research.
fetch	Reads a section in full, with its code, link, and citation. Compatible with ChatGPT deep research.
get_code	Runnable R, Python, or Julia code for a task, with the packages it uses.
cite	References in APA, Chicago, MLA, Harvard, BibTeX, or RIS for the sections consulted, deduplicated by work.
list_sources	The books and packages, with topics, languages, licenses, and links.

Prompts (slash commands in clients that support them): plan_analysis, write_methods_section, explain_method. Resources: the library catalog and a table of contents for each work.

All tools are read-only. The server makes no calls to language models and stores no information about its users; see the privacy policy.

Sources

Work	Code	Served	License
A Guide on Data Analysis	R	Full text	CC BY-NC-SA 4.0
causalverse: The All-in-One Causal Inference Toolkit	R	Full text and function reference	GPL-3
Credit Scoring: Theory, Methods, and Practice	Python	Full text	CC BY 4.0 text, MIT code
Marketing Research: A Technical and Academic Treatment	R	Excerpts and code; full text on the website	All rights reserved text, MIT code
AI in Action: A Reference for Artificial Intelligence	Python	Excerpts and code; full text on the website	All rights reserved

Chapters of A Guide on Data Analysis that appear in the Springer print volumes are cited as those volumes, for example Nguyen, M. (2025). Foundations of data analysis (Vol. 1). Springer Cham. https://doi.org/10.1007/978-3-032-01858-8.

Method

The source files of each work (.Rmd, .qmd, .Rd) are parsed into a single index of sections, code blocks, and citations. During index construction every link is verified against the published website: a section whose anchor is absent links to its chapter page, and unpublished pages are excluded. Queries are answered from the in-memory index; no database or language model is involved.

Self-hosting

docker build -t research-methods-mcp .
docker run -p 8080:8080 research-methods-mcp     # http://localhost:8080/mcp

Or with Node.js: npm ci && npm run build && npm start (listens on 127.0.0.1:8787; set HOST and PORT to change). The same handler runs on Deno Deploy through src/deno.ts.

Development

npm ci
npm run build:index   # parse the book repositories (sibling folders) into data/index.json.gz
npm run build         # bundle dist/stdio.js and dist/http.js
npm test              # end-to-end tests through real MCP clients
npm run check-links   # audit every link in the index against the live sites

To add a book, add an entry to sources.yml and rebuild the index. To add or tune a method, edit methods.yml; the build fails if a method points at a section that does not exist.

Citation

Users are asked to cite the works they draw on; the cite tool formats the references. To cite the server itself, see CITATION.cff.

License

The server code is MIT licensed. The content it returns keeps each work's own license, listed above and by the list_sources tool.