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mcp-crossref

by algonacci

get_crossref_author

Build an author profile from Crossref metadata: publications, years active, co-authors, venues, affiliations, and ORCID iDs. Use it to identify a researcher's work and collaborators.

Instructions

Build an author profile: publications, years active, frequent co-authors, venues, affiliations
and ORCID iDs seen in Crossref metadata.

When to use:
    - "Who is this researcher / what do they work on / who do they collaborate with?"
    - Finding collaborators or research groups around a person.

Args:
    name: Author name, e.g. "Geoffrey Hinton". Matching is fuzzy, so namesakes can be mixed in.
    orcid: ORCID iD (e.g. "0000-0002-1825-0097"). When given, only works carrying this iD are used,
        which removes namesakes. If the result lists several ORCID iDs, rerun with one of them.
    max_works: Works to scan, 20-500 (default 100).

Returns:
    {"name", "orcid_filter", "scanned", "matched", "total_citations", "years": {year: count},
     "orcids_seen", "affiliations", "coauthors", "venues": [[name, count]],
     "most_cited": [compact work]}

Counts describe Crossref metadata, not research impact; centrality is not quality.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
orcidNo
max_worksNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it warns that name matching is fuzzy and namesakes can be mixed in, explains that an ORCID filter removes them and that multiple returned ORCIDs signal a rerun, caps max_works at 20-500, and cautions that counts reflect Crossref metadata rather than research impact.

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?

The sectioned layout (When to use / Args / Returns) is front-loaded and scannable, and most lines earn their place. The Returns block largely restates the output schema, which is mildly redundant, though it is compact and does add the impact caveat.

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?

Given an output schema exists, return values need not be re-explained; the description instead covers the argument semantics, usage triggers, and the fuzzy-matching/namesake caveats that structured fields cannot convey. Nothing an agent needs to invoke it correctly is missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate entirely, and it does: each of the three parameters gets meaning beyond its type, including a concrete name example, the ORCID filtering semantics plus a sample iD, and the max_works range with default.

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 states a specific verb (build) and resource (author profile from Crossref metadata), enumerating the exact fields produced (publications, co-authors, venues, affiliations, ORCID iDs). This is clearly distinct from siblings like get_crossref_work or search_crossref, which target works rather than people.

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 'When to use' block gives concrete triggering questions ("Who is this researcher / who do they collaborate with?") and the ORCID workflow tells the agent how to iterate when namesakes appear. It stops short of naming alternative siblings or stating when NOT to use this tool, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.