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YGao2005

Scholar Feed MCP Server

co_author_graph

Map the co-authorship network for one or more authors. Identify collaboration ties, shared papers, and recent collaborations within a specified time window.

Instructions

Find the co-authorship neighborhood of one or more authors. Given a list of author_ids, returns edges {from, to, papers_count, last_collab_year} where 'from' is one of the input authors and 'to' is any co-author appearing on a shared paper within the window. Use for AC reviewer triage (find conflicts), disambiguating researchers (who do they actually work with?), or expanding an author seed into a research community. window_years defaults to 10. Result is capped at 500 edges, sorted by papers_count DESC.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
author_idsYesAuthor IDs to query (1-25). Get author IDs via the find_author tool.
window_yearsNoOnly count co-authorships from the last N years (default 10, max 30).
Behavior3/5

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

Description discloses defaults, cap of 500 edges, and sorting order, but does not cover error handling or behavior for invalid inputs, which would be valuable given no annotations.

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 clear sentences with a bullet list of use cases; no wasted words and front-loaded with purpose.

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?

Given no output schema, description adequately explains output shape and constraints; could mention result size limit and error conditions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds meaning by explaining how to obtain author_ids and the default/max for window_years.

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 the tool finds the co-authorship neighborhood of one or more authors, specifies input and output structure, and distinguishes from siblings such as find_author.

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?

Explicit use cases are given (reviewer triage, disambiguation, community expansion), but no comparison to alternative tools or 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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