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CutGPT Research & Fact-Check

Search scholarly papers

search_papers
Read-onlyIdempotent

Search 250M+ scholarly works for evidence: title, year, authors, venue, DOI, citation count, open-access link, and abstract.

Uses OpenAlex (falls back to Crossref). Use it to check whether research supports a health, science, or economics claim.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYesResearch topic or question, e.g. 'intermittent fasting weight loss'.
from_yearNoOnly papers published in or after this year.
open_access_onlyNoOnly papers with a free full-text link.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, so the safety profile is covered. The description adds real behavioral value beyond that: the upstream source is OpenAlex with a Crossref fallback, and it enumerates what comes back. It omits rate limits, pagination, and latency, but with an output schema present those gaps are minor.

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

Conciseness5/5

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

Two compact sentences with zero filler: the capability and return fields come first, followed by the data source and the use case. Every clause carries information.

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

Completeness4/5

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

With rich annotations, an output schema, and mostly documented parameters, the description covers the essentials: what it searches, where the data comes from, and what is returned. It stops short of noting the limit cap or result ordering, which would fully close the loop for a 4-parameter tool.

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 coverage is 75%, so the schema already documents query (with an example), from_year, and open_access_only. The description adds no parameter-level meaning beyond the schema and says nothing about limit or its 1-20 cap, so the baseline 3 applies.

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

Purpose5/5

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

States a specific verb (search) and resource (250M+ scholarly works) and enumerates the returned fields (title, year, authors, venue, DOI, citation count, open-access link, abstract). No sibling tool performs literature search, so it is trivially distinguishable from wikipedia_lookup, find_fact_checks, and the assistant-recommendation tools.

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

Usage Guidelines4/5

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

Explicitly names the intended use case: checking whether research supports a health, science, or economics claim. It gives clear context for when to reach for this tool but does not mention alternatives (e.g., find_fact_checks or wikipedia_lookup) or state when not to use it.

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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