Research Methods Library

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io.github.mikenguyen13/research-methods-mcpv0.1.1

Research methods for AI assistants: study design guidance, R and Python code, and citations

Research & Knowledge

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choose_method Read-only

Recommend statistical, econometric, causal inference, or machine learning methods for a research question or data analysis task: which statistical test or model fits the data, how to estimate a causal or treatment effect, how to analyze an A/B test or experiment, how many observations are needed (power and sample size), how to handle panel, time-series, survey, text, or survival data, and how to build predictive or credit-scoring models. Describe the question, the data (cross-section, panel, time series, experiment), how the treatment or exposure was assigned, and the outcome. Returns candidate methods with when each applies, key assumptions, R and Python packages, and the library sections that teach them.

questionstring, required

The research question and setting, e.g. "effect of a minimum wage increase that some states adopted in different years on teen employment".

limitinteger

Number of methods (default 3).

languagestring

Only list packages for this language.

Tools (6)

choose_method

read-only

Argument	Type	Description
limit	integer	Number of methods (default 3).
language	string	Only list packages for this language.
question*	string	The research question and setting, e.g. "effect of a minimum wage increase that some states adopted in different years on teen employment".

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "question"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 6,
      "minimum": 1,
      "description": "Number of methods (default 3)."
    },
    "language": {
      "enum": [
        "r",
        "python",
        "julia"
      ],
      "type": "string",
      "description": "Only list packages for this language."
    },
    "question": {
      "type": "string",
      "minLength": 3,
      "description": "The research question and setting, e.g. \"effect of a minimum wage increase that some states adopted in different years on teen employment\"."
    }
  }
}

cite

Format references for works in the library in APA, Chicago, MLA, Harvard, BibTeX, or RIS. Pass the ids of the sections you used to get a deduplicated reference list plus the section links, or pass source ids to cite whole works. With no ids, returns a reference for every work in the library.

read-only

Argument	Type	Description
ids	array	Section ids, or source ids: data_analysis, causalverse, credit_score, marketing_research, ai_in_action, advanced_data_analysis, tidy_finance_vn, tidy_finance_vn_vi, mlpy, publications.
style	string	Citation style (default apa).

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "ids": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Section ids, or source ids: data_analysis, causalverse, credit_score, marketing_research, ai_in_action, advanced_data_analysis, tidy_finance_vn, tidy_finance_vn_vi, mlpy, publications."
    },
    "style": {
      "enum": [
        "apa",
        "chicago",
        "mla",
        "harvard",
        "bibtex",
        "ris"
      ],
      "type": "string",
      "description": "Citation style (default apa)."
    }
  }
}

fetch

Read one section of the library by its section id. Returns the section in Markdown with its code, the section URL, and the citation of the work it comes from. Long sections come in parts; the text names the id of the next part.

read-only

Argument	Type	Description
id*	string	Section id, e.g. "data_analysis:sec-regression-discontinuity".

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "id"
  ],
  "properties": {
    "id": {
      "type": "string",
      "minLength": 1,
      "description": "Section id, e.g. \"data_analysis:sec-regression-discontinuity\"."
    }
  }
}

get_code

Get runnable code from the library for a specific statistical, econometric, causal inference, or machine learning task, in R, Python, or Julia. Suited to requests for code that carries out a named estimator, test, or modeling step; it returns code with brief context, not an explanation of the method. Results can be restricted to one language or one work. Returns code blocks with the section they come from, the packages they use, a link, and a citation.

read-only

Argument	Type	Description
limit	integer	Number of sections to return code from (default 4).
topic*	string	The task, e.g. "Callaway and Sant'Anna group-time ATT", "rdrobust bandwidth", "WoE binning scorecard".
source	string	Limit to one work: data_analysis (A Guide on Data Analysis); causalverse (causalverse: The All-in-One Causal Inference Toolkit); credit_score (Credit Scoring: Theory, Methods, and Practice); marketing_research (Marketing Research: A Technical and Academic Treatment); ai_in_action (AI in Action: A Reference for Artificial Intelligence); advanced_data_analysis (Advanced Data Analysis); tidy_finance_vn (Applying Tidy Finance with Python to Vietnam); tidy_finance_vn_vi (Tài Chính Ứng Dụng với Python); mlpy (Machine Learning in Python); publications (Research Articles and Publications).
language	string	Programming language of the code: r, python, or julia.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "topic"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 10,
      "minimum": 1,
      "description": "Number of sections to return code from (default 4)."
    },
    "topic": {
      "type": "string",
      "minLength": 2,
      "description": "The task, e.g. \"Callaway and Sant'Anna group-time ATT\", \"rdrobust bandwidth\", \"WoE binning scorecard\"."
    },
    "source": {
      "enum": [
        "data_analysis",
        "causalverse",
        "credit_score",
        "marketing_research",
        "ai_in_action",
        "advanced_data_analysis",
        "tidy_finance_vn",
        "tidy_finance_vn_vi",
        "mlpy",
        "publications"
      ],
      "type": "string",
      "description": "Limit to one work: data_analysis (A Guide on Data Analysis); causalverse (causalverse: The All-in-One Causal Inference Toolkit); credit_score (Credit Scoring: Theory, Methods, and Practice); marketing_research (Marketing Research: A Technical and Academic Treatment); ai_in_action (AI in Action: A Reference for Artificial Intelligence); advanced_data_analysis (Advanced Data Analysis); tidy_finance_vn (Applying Tidy Finance with Python to Vietnam); tidy_finance_vn_vi (Tài Chính Ứng Dụng với Python); mlpy (Machine Learning in Python); publications (Research Articles and Publications)."
    },
    "language": {
      "enum": [
        "r",
        "python",
        "julia"
      ],
      "type": "string",
      "description": "Programming language of the code: r, python, or julia."
    }
  }
}

list_sources

List the works in the library (textbooks, an R package, and research articles) with their topics, code languages, section counts, licenses, links, and how to cite each one. Suited to questions about what the library covers or which work to consult. Takes no parameters and returns one entry per work.

read-only

No arguments.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {}
}

Search sections of textbooks and an R package on research methods and data analysis: statistics and hypothesis testing, regression and generalized linear models, causal inference (difference-in-differences, synthetic control, regression discontinuity, instrumental variables, matching, double machine learning), econometrics, experimental design, survey methods, marketing research, credit scoring, machine learning, and AI. Returns matching sections with their ids, a link, a snippet, the code languages they contain, and a citation.

read-only

Argument	Type	Description
limit	integer	Number of results (default 8).
query*	string	What to look for, in plain words, e.g. "staggered difference-in-differences with never-treated controls".
source	string	Limit to one work: data_analysis (A Guide on Data Analysis); causalverse (causalverse: The All-in-One Causal Inference Toolkit); credit_score (Credit Scoring: Theory, Methods, and Practice); marketing_research (Marketing Research: A Technical and Academic Treatment); ai_in_action (AI in Action: A Reference for Artificial Intelligence); advanced_data_analysis (Advanced Data Analysis); tidy_finance_vn (Applying Tidy Finance with Python to Vietnam); tidy_finance_vn_vi (Tài Chính Ứng Dụng với Python); mlpy (Machine Learning in Python); publications (Research Articles and Publications).
language	string	Prefer sections with code in this language.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "query"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 25,
      "minimum": 1,
      "description": "Number of results (default 8)."
    },
    "query": {
      "type": "string",
      "minLength": 1,
      "description": "What to look for, in plain words, e.g. \"staggered difference-in-differences with never-treated controls\"."
    },
    "source": {
      "enum": [
        "data_analysis",
        "causalverse",
        "credit_score",
        "marketing_research",
        "ai_in_action",
        "advanced_data_analysis",
        "tidy_finance_vn",
        "tidy_finance_vn_vi",
        "mlpy",
        "publications"
      ],
      "type": "string",
      "description": "Limit to one work: data_analysis (A Guide on Data Analysis); causalverse (causalverse: The All-in-One Causal Inference Toolkit); credit_score (Credit Scoring: Theory, Methods, and Practice); marketing_research (Marketing Research: A Technical and Academic Treatment); ai_in_action (AI in Action: A Reference for Artificial Intelligence); advanced_data_analysis (Advanced Data Analysis); tidy_finance_vn (Applying Tidy Finance with Python to Vietnam); tidy_finance_vn_vi (Tài Chính Ứng Dụng với Python); mlpy (Machine Learning in Python); publications (Research Articles and Publications)."
    },
    "language": {
      "enum": [
        "r",
        "python",
        "julia"
      ],
      "type": "string",
      "description": "Prefer sections with code in this language."
    }
  }
}