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research-paper-search

Search across 250M+ academic papers to find relevant research. Filter by open access, year range, and sort by relevance, citations, or recency.

Instructions

Academic paper search across 250M+ works via OpenAlex (free, no key). Returns top papers with title, authors, year, DOI, citation count, open-access status, and primary research topic. Covers all disciplines: AI/ML, medicine, physics, economics, law, biology, and more. Supports relevance, citation-count, and recency sorting; open-access filtering; and year-range constraints. Use for literature review, prior-art search, citation building, or finding the seminal papers in any field.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoSearch query. Natural-language or keyword (e.g. 'transformer attention mechanism', 'CRISPR gene editing cancer', 'bitcoin game theory').
limitNoNumber of papers to return (1–10). Default: 5.
sortNoSort order. relevant = OpenAlex relevance score; cited = most-cited first; recent = newest first. Default: relevant.
min_yearNoOnly return papers published this year or later (e.g. 2020). Optional.
open_access_onlyNoIf true, only return open-access papers with freely available PDFs. Default: false.
Behavior3/5

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

Discloses free usage, no API key, and return fields. Missing details on rate limits, pagination, or error handling. With no annotations, description carries full burden for transparency; partial coverage.

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?

Two sentences plus a list of use cases. Efficient and front-loaded with key information. No irrelevant details, but could be slightly more compact.

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?

Describes return fields and covers input constraints. With no output schema, the description adequately informs about output structure. Missing pagination details, but overall complete for a search 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 100%, so baseline 3. Description adds context for sort meaning and open-access filtering but does not significantly extend beyond schema descriptions.

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?

The description clearly states the tool performs academic paper search across 250M+ works via OpenAlex. It specifies the resource (academic papers) and action (search), and is distinct from sibling tools like arxiv-intel.

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 lists use cases: literature review, prior-art search, citation building, finding seminal papers. Does not provide when-not-to-use or alternative tools, but context is clear.

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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