research-methods-mcp

endpoint

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

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is research-methods-mcp live?

Yes — it answered the hub's last check (checked 11m ago). It answered 100% of checks over the last 30 days.

Is research-methods-mcp free to use?

Yes — the hub reached it with no key and no payment.

What tools does research-methods-mcp have?

6 tools: search, fetch, get_code, choose_method, cite, list_sources.

Is research-methods-mcp safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• cite open 11m ago

  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.

  mcp-tool

  12345678910111213141516171819202122232425

  {
    "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, social_theory, publications."
      },
      "style": {
        "enum": [
          "apa",
          "chicago",
          "mla",
          "harvard",
          "bibtex",
          "ris"
        ],
        "type": "string",
        "description": "Citation style (default apa)."
      }
    }
  }

  arguments 25 lines copy
• list_sources open 11m ago

  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.

  mcp-tool

  12345

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

  arguments 5 lines copy
• search unknown never probed

  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.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546

  {
    "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",
          "social_theory",
          "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); social_theory (Social Science Theory: A Field Guide to the Theories, Effects, and Frameworks Behind Research and Business); publications (Research Articles and Publications)."
      },
      "language": {
        "enum": [
          "r",
          "python",
          "julia"
        ],
        "type": "string",
        "description": "Prefer sections with code in this language."
      }
    }
  }

  arguments 46 lines copy
• fetch unknown never probed

  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.

  mcp-tool

  1234567891011121314

  {
    "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\"."
      }
    }
  }

  arguments 14 lines copy
• get_code unknown never probed

  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.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546

  {
    "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",
          "social_theory",
          "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); social_theory (Social Science Theory: A Field Guide to the Theories, Effects, and Frameworks Behind Research and Business); publications (Research Articles and Publications)."
      },
      "language": {
        "enum": [
          "r",
          "python",
          "julia"
        ],
        "type": "string",
        "description": "Programming language of the code: r, python, or julia."
      }
    }
  }

  arguments 46 lines copy
• choose_method unknown 11m ago

  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.

  mcp-tool

  1234567891011121314151617181920212223242526272829

  {
    "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\"."
      }
    }
  }

  arguments 29 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.