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Find research evidence

find_evidence
Read-only

Find scholarly evidence by searching 322M works from a plain-language claim or topic, returning titles, years, citation counts, venues, URLs, and abstracts.

Instructions

Semantic search over 322M scholarly works: a claim or topic in, works out with title, year, citation count, venue, url and abstract. Cite only what the abstract supports. Costs a fraction of a cent (about $0.001) from credit; budgetUsd (default 2, max 10) caps it and chargedUsd says what it cost. Needs the write scope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe claim or topic in plain language, as you would say it out loud.
min_yearNoDrop work published before this year.
budgetUsdNoOptional: the most this call may spend, in dollars, default 2, max 10.
max_resultsNoHow many works to return (1-25, default 10).
min_citationsNoDrop work cited fewer times than this.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYes
mediaYes
outputNoThe tool's own result, shaped by the tool.
chargedUsdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.4

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/non-destructive, but the description adds substantial behavior beyond them: the per-call cost (~$0.001), the budgetUsd cap semantics, the chargedUsd return field, and an auth requirement ('Needs the write scope'). The write-scope note sits in mild tension with readOnlyHint but describes authorization, not mutation, so it is not a true contradiction.

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?

Three dense sentences, front-loaded with purpose and scale, then output shape, then cost/budget/auth. Every clause carries information; nothing is padding.

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?

For a search tool with an output schema, full param coverage and rich annotations, the description supplies the remaining agent-relevant context: the citation-integrity caveat and the cost/budget model. Nothing needed to call it correctly 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 coverage is 100%, so the schema already documents all five parameters with descriptions. The description restates budgetUsd defaults/max but adds no new parameter semantics (min_year, min_citations, max_results are untouched). Baseline 3 is appropriate when the schema does the work.

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 and resource — semantic search over 322M scholarly works — and describes the exact input ('a claim or topic in') and output shape ('title, year, citation count, venue, url and abstract'). This clearly distinguishes it from media-search siblings like find_photo/find_footage and from web_search, all without opening a schema.

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 framing ('a claim or topic in, works out') and the downstream caveat 'Cite only what the abstract supports', but no alternative sibling is named and no explicit when-to-use/when-not condition is given (e.g. vs web_search or news_search). Adequate but leaves routing to inference.

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