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mcp-crossref

by algonacci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_crossref_funder

Look up a research funder by name or Crossref Funder ID and retrieve its funded works, counts per year, and top venues. Use to track grant output or explore a funder's landscape.

Instructions

Look up a research funder and the works it funded (as declared by publishers in Crossref).

When to use:
    - "What has LPDP / NSF / Horizon Europe funded?", grant output tracking, funder landscape.

Args:
    name_or_id: Funder name ("LPDP", "National Science Foundation") or Crossref Funder ID
        ("501100014538").
    latest: Number of most recent funded works to include (0-20, default 5).

Returns:
    {"funder": {"id", "name", "location", "alt_names"}, "other_matches", "funded_works_total",
     "per_year": {year: count}, "top_venues": [[name, count]], "latest": [compact work]}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latestNo
name_or_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It implies a read-only lookup and usefully discloses that funding links are 'as declared by publishers', plus the ambiguity signal via 'other_matches', but says nothing about auth requirements, rate limits, or failure behavior for an unknown funder.

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?

Front-loaded purpose followed by clearly labelled When to use / Args / Returns sections; every element is scannable. The Returns block is somewhat redundant given a formal output schema exists, which keeps it just short of a 5.

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?

For a 2-parameter, output-schema-backed tool this covers the essentials: what it returns at a glance, parameter formats, and the multi-match case. Missing only the read-only/auth framing that the absent annotations would otherwise have provided.

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 coverage is 0%, so the description must carry the load and it does: name_or_id is explained as accepting either a funder name or a Crossref Funder ID with a concrete ID example ('501100014538'), and latest is bounded ('0-20, default 5') beyond the bare schema 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?

States a specific verb ('look up') plus the resource ('a research funder') and its payload ('the works it funded'), and pins the data provenance to Crossref publisher declarations. The funder resource is clearly distinct from the sibling author/journal/work lookup tools.

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?

An explicit 'When to use' block names concrete scenarios ('What has LPDP / NSF / Horizon Europe funded?', grant output tracking, funder landscape). It gives clear positive context but names no alternatives or when-not conditions (e.g. when to use search_crossref instead).

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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