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mcp-crossref

by algonacci
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Python
Remote

get_crossref_journal

Look up a journal by ISSN or name to get publisher, ISSNs, subjects, DOI counts, metadata coverage, and recent works. Use it to find ISSNs or assess deposited abstracts, ORCIDs, and references.

Instructions

Look up a journal: publisher, ISSNs, subjects, DOI counts, metadata coverage and latest works.

When to use:
    - "Tell me about journal X", "what does X publish lately?", "does X deposit abstracts/ORCIDs?"
    - Finding a journal's ISSN from its name (then filter search_crossref by issn).

Args:
    issn_or_name: An ISSN like "0957-4174" for the full profile, or a name like
        "expert systems with applications" to list matching journals with their ISSNs.
    latest: Number of most recent works to include for an ISSN lookup (0-20, default 5).

Returns:
    For an ISSN: {"title", "publisher", "issn", "subjects", "total_dois", "coverage" (share of
    current works with abstracts, ORCIDs, references, licenses, ...), "dois_by_year", "latest"}.
    For a name: {"matches": [{"title", "publisher", "issn", "total_dois"}]}.

Coverage is metadata completeness, not a journal quality ranking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latestNo
issn_or_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it discloses the two distinct return shapes, the meaning of "coverage" (share of current works with abstracts/ORCIDs/references/licenses), the latest range and default, and a critical caveat that coverage is metadata completeness, not a quality ranking. It omits operational traits such as rate limits or auth, but for a public read-only metadata lookup this is close to complete.

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?

Front-loaded with the one-line purpose, then cleanly sectioned into When to use, Args, and Returns. Every sentence adds information an agent needs (trigger conditions, dual-mode argument behavior, return shapes, the coverage caveat) with no filler.

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?

For a two-parameter read tool with an output schema present, the description supplies everything an agent needs: when to call it, the dual interpretation of the primary argument, the return structure per mode, and the semantic caveat on coverage. Nothing required to invoke or interpret 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 fully and it does: issn_or_name is explained with a concrete example ("0957-4174") and the behavioral difference between passing an ISSN versus a name (full profile vs. list of matches), and latest is documented as 0-20 with a default of 5. Both parameters gain meaning absent from the schema.

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 opening line states a specific verb (look up) and resource (a journal) and enumerates exactly what is retrieved: publisher, ISSNs, subjects, DOI counts, metadata coverage and latest works. This is clearly distinguishable from siblings like get_crossref_work or get_crossref_author, which operate on works and people rather than journals.

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?

It gives explicit trigger phrasings ("Tell me about journal X", "does X deposit abstracts/ORCIDs?") and names a concrete downstream alternative: use the returned ISSN to filter search_crossref by issn. When to use and how it chains into a sibling are both stated rather than left to inference.

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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