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oksure

OpenAlex Research MCP Server

by oksure

search_authors

Search for authors and researchers by name. Returns h-index, citation count, and affiliation data. Filter by institution, works count, citation count, or sort results. Ideal for locating subject matter experts.

Instructions

Search for authors/researchers. Returns h-index, citation count, and affiliation data. Best for finding experts when you know the name. Use search_authors_by_expertise to find experts by research area.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoAuthor name or search query. For exact phrase matching of a full name (e.g., 'Sarah Jane Williams') or a concept (e.g., 'deep reinforcement learning'), set exact_phrase to true.
exact_phraseNoSet to true for exact phrase matching. Without this, name/query tokens are matched independently. Use this when the query is a full name (e.g., 'Anna Maria Bianchi') or a specific multi-word concept.
works_countNoFilter by number of works. Use >X or <X. Example: ">50"
cited_by_countNoFilter by total citation count. Use >X or <X. Example: ">1000"
institutionNoFilter by institution name or ID
sortNoSort results: cited_by_count:desc (default), works_count:desc, publication_year:desc
per_pageNoResults per page (default: 10, max: 200)
Behavior4/5

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

No annotations provided, but description clarifies read-only nature (returns data) and behavior (name-based search). Could be more explicit about sorting and pagination defaults, but schema covers those. No side effects or auth issues mentioned, but likely unnecessary.

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 succinct sentences: purpose, return data, usage context, alternative. No fluff. Front-loaded with essential 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?

No output schema, but description mentions key return fields (h-index, citation count, affiliation). Lacks mention of other typical fields like author ID, but adequate for a search tool. Pagination implied via schema per_page.

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 baseline is 3. Description does not add parameter-specific details beyond schema, but it summarizes return data relevant to parameters. No need for higher score.

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?

Description clearly states action (Search) and resource (authors/researchers), specifies return data (h-index, citation count, affiliation), and distinguishes from sibling tool 'search_authors_by_expertise' by noting when to use each.

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

Usage Guidelines5/5

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

Explicit guidance: 'Best for finding experts when you know the name. Use search_authors_by_expertise to find experts by research area.' Clearly states usage context and alternative.

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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