Scholar Feed

by io.github.YGao2005

Co-Author Graph

co_author_graph

Read-only

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "author_ids": "<author_ids>"
    },
    "name": "co_author_graph"
  }
}

Input Schema

Name	Required	Description	Default
`author_ids`	Yes	Author IDs to query (1-25). Get author IDs via the find_author tool.
`window_years`	No	Only count co-authorships from the last N years (default 10, max 30).

Output Schema

Name	Required	Description
`edges`	No	Co-authorship edges {from, to, papers_count, last_collab_year}.
`edge_count`	No
`window_years`	No
`queried_author_ids`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 6, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses specific behavior: results are capped at 500 edges, sorted by papers_count DESC, and computed within the window_years window. It also clarifies edge direction semantics ('from' is an input author, 'to' is a co-author). These are valuable operational details not in the annotation.

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 about 80 words and front-loads the core purpose, then adds the output shape, use cases, and key limits. No filler or redundancy.

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?

The tool is a read-only query with 100% parameter documentation and an output schema. The description adds the cap, ordering, default window, and use cases, leaving no critical behavior uncovered.

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 baseline is 3. The description links author_ids to the 'from' output field and window_years to the 'within the window' constraint, but those semantics are largely inferable from the schema. No substantial parameter nuance is added.

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 first sentence names a specific verb and resource: 'Find the co-authorship neighborhood of one or more authors.' It goes on to define the exact edge structure, and no sibling tool covers co-authorship graphs, so it is easy to distinguish.

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 lists three explicit use cases: AC reviewer triage, researcher disambiguation, and expanding seed authors into a community. It also points to find_author as the source for author IDs in the schema, giving a prerequisite. It doesn't explicitly mention when not to use the tool, but the use-case list is clear context.

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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