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Research Methods Library

by io.github.mikenguyen13
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Search the research methods library

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Read-onlyIdempotent

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.

Input Schema

NameRequiredDescriptionDefault
limitNoNumber of results (default 8).
queryYesWhat to look for, in plain words, e.g. "staggered difference-in-differences with never-treated controls".
sourceNoLimit 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).
languageNoPrefer sections with code in this language.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / source / description
      Previous value: -"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)."New value: +"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)."
    • changedInput schema / properties / source / enum
      Previous value: -[
-  "data_analysis",
-  "causalverse",
-  "credit_score",
-  "marketing_research",
-  "ai_in_action",
-  "advanced_data_analysis",
-  "tidy_finance_vn",
-  "tidy_finance_vn_vi",
-  "mlpy",
-  "publications"
-]New value: +[
+  "data_analysis",
+  "causalverse",
+  "credit_score",
+  "marketing_research",
+  "ai_in_action",
+  "advanced_data_analysis",
+  "tidy_finance_vn",
+  "tidy_finance_vn_vi",
+  "mlpy",
+  "social_theory",
+  "publications"
+]
  2. Changed2 schema fields changed
    • changedInput schema / properties / source / description
      Previous value: -"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); comm_theory (Communication Theory); publications (Research Articles and Publications)."New value: +"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)."
    • changedInput schema / properties / source / enum
      Previous value: -[
-  "data_analysis",
-  "causalverse",
-  "credit_score",
-  "marketing_research",
-  "ai_in_action",
-  "advanced_data_analysis",
-  "tidy_finance_vn",
-  "tidy_finance_vn_vi",
-  "mlpy",
-  "comm_theory",
-  "publications"
-]New value: +[
+  "data_analysis",
+  "causalverse",
+  "credit_score",
+  "marketing_research",
+  "ai_in_action",
+  "advanced_data_analysis",
+  "tidy_finance_vn",
+  "tidy_finance_vn_vi",
+  "mlpy",
+  "publications"
+]
  3. Changed2 schema fields changed
    • changedInput schema / properties / source / description
      Previous value: -"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); publications (Research Articles and Publications)."New value: +"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); comm_theory (Communication Theory); publications (Research Articles and Publications)."
    • changedInput schema / properties / source / enum
      Previous value: -[
-  "data_analysis",
-  "causalverse",
-  "credit_score",
-  "marketing_research",
-  "ai_in_action",
-  "publications"
-]New value: +[
+  "data_analysis",
+  "causalverse",
+  "credit_score",
+  "marketing_research",
+  "ai_in_action",
+  "advanced_data_analysis",
+  "tidy_finance_vn",
+  "tidy_finance_vn_vi",
+  "mlpy",
+  "comm_theory",
+  "publications"
+]
  4. Changed2 schema fields changed
    • changedInput schema / properties / source / description
      Previous value: -"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)."New value: +"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); publications (Research Articles and Publications)."
    • changedInput schema / properties / source / enum
      Previous value: -[
-  "data_analysis",
-  "causalverse",
-  "credit_score",
-  "marketing_research",
-  "ai_in_action"
-]New value: +[
+  "data_analysis",
+  "causalverse",
+  "credit_score",
+  "marketing_research",
+  "ai_in_action",
+  "publications"
+]
  5. First observed

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, closed-world, non-destructive, so the safety profile is covered. The description adds value beyond that by disclosing the return contents (ids, link, snippet, code languages, citation) in the absence of an output schema, and by defining the corpus boundary. It does not discuss ranking, relevance behavior, or what happens when no sections match.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the search verb and resource, followed by a return-value sentence that earns its place. The mid-sentence topic enumeration is long and somewhat sprawling, but it doubles as corpus scoping rather than pure filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by stating exactly what a result contains (id, link, snippet, code languages, citation), which is what an agent needs to chain into fetch or cite. Combined with 100% schema coverage on the four parameters and annotations covering safety, nothing essential to correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents query, limit, source, and language, including the full source enum with work titles. The description mentions the corpus at a high level but adds no syntax, ranking, or preference semantics beyond the schema (e.g., how 'language' preference interacts with results). Baseline 3 applies when the schema carries the parameter burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a concrete verb-plus-resource: it searches "sections of textbooks and an R package," then enumerates the topical scope (regression, causal inference, econometrics, etc.). An agent can tell this is full-text retrieval over a curated corpus rather than document retrieval of a whole work. It never names the sibling tools (fetch, get_code, cite), so the differentiation is inferable but not stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the scope statement: search this corpus when the question touches research methods, statistics, causal inference, or the listed applied domains. There is no explicit when-not guidance and no routing to alternatives such as fetching a full section once an id is found, or using get_code for runnable code. An agent must infer the search-then-fetch workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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