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oksure

OpenAlex Research MCP Server

by oksure

search_authors_by_expertise

Identify leading researchers in any research topic, ranked by h-index or citation count. Filter by institution or minimum citations to find experts in a specific field.

Instructions

Find leading researchers/experts in a specific topic or research area, ranked by h-index or citation count. More useful than search_authors when you do not know names but need to identify key figures in a field.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesResearch topic or field (e.g., "transformer models", "CRISPR gene editing"). For exact phrase matching (e.g., 'privacy paradox' as a specific concept), set exact_phrase to true.
exact_phraseNoSet to true for exact phrase matching. Without this, search terms are matched independently. Use this when searching for a specific concept or multi-word term (e.g., 'privacy paradox', 'supply chain resilience').
min_h_indexNoMinimum h-index to filter senior researchers (e.g., 20 for established researchers)
min_cited_by_countNoMinimum total citations (alternative to min_h_index)
institutionNoFilter by institution
per_pageNoResults per page (default: 10, max: 50)
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It mentions ranking by h-index or citation count and suggests parameter usage, but lacks details on pagination, sorting behavior, output structure, or request limits.

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?

The description is two sentences long, front-loaded with purpose, and contains no extraneous information. Every sentence adds value.

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 6 parameters all documented in schema and no output schema, the description adequately covers the functionality and usage context. Some organizational details (e.g., data freshness, rate limits) are absent, but not critical 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 description coverage is 100%, so the input schema already documents each parameter with descriptions. The tool description adds no additional meaning beyond what the schema provides, meeting the baseline of 3.

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 finds leading researchers/experts in a specific topic, ranked by h-index or citation count. It distinguishes itself from search_authors by targeting name-unknown scenarios.

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?

The description explicitly says 'More useful than search_authors when you do not know names but need to identify key figures in a field.' This provides clear when-to-use guidance, though alternatives beyond the one sibling are not mentioned.

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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