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algonacci

mcp-crossref

by algonacci

resolve_dois

Extract DOIs from messy text and resolve them to clean metadata, verifying existence and enabling citation formatting. Ideal for cleaning reference lists or checking DOIs before citing.

Instructions

Find every DOI mentioned in free text and resolve each one to clean metadata.

When to use:
    - The user pastes a messy reference list, a PDF's text, notes or a URL list and wants to know
      what the papers are, check that the DOIs exist, or turn them into a clean table.
    - Verifying DOIs produced by another tool or model before citing them.

Args:
    text: Any text containing DOIs in any form ("doi:10.x/y", "https://doi.org/10.x/y", bare).
    max_dois: Resolve at most this many unique DOIs (default 25, max 100).

Returns:
    {"found": int, "works": [compact work], "failed": [{"doi", "error"}]}. A DOI in "failed"
    with "Not found" is unknown to Crossref (typo, fabricated, or registered with DataCite).

Follow-up: pass the resolved DOIs to cite_dois to format a bibliography.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
max_doisNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
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 well: it discloses the failure mode and its meaning ('Not found' = typo, fabricated, or DataCite-registered), the cap behavior (default 25, max 100), and that dedup applies to unique DOIs. It stops short of stating network/auth behavior or partial-failure semantics beyond the failed list.

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 sentence followed by tightly scoped Args/Returns/Follow-up sections; every block earns its place. The Returns block partially restates the output schema, which slightly blunts conciseness, but the 'Not found' interpretation is genuinely additive.

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?

An output schema exists, so the shape need not be re-explained, yet the description usefully clarifies the meaning of the 'failed' entries and points to the next tool. Coverage is complete for an agent to call this correctly; only edge-case behavior (e.g., partial results on network failure) is absent.

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 description coverage is 0%, so the description must compensate, and it does: text accepts any DOI form ('doi:10.x/y', 'https://doi.org/10.x/y', bare) and max_dois is documented with default and ceiling. Minor gap: no guidance on behavior when max_dois exceeds the number found.

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 (find, resolve) and resource (DOIs in free text) plus the outcome (clean metadata). It is clearly distinguishable from siblings like get_crossref_work (single work lookup) and cite_dois (bibliography formatting).

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?

The 'When to use' block gives two concrete scenarios (messy reference list/PDF text/URL list, and verifying DOIs from another tool or model) and the closing follow-up explicitly routes to cite_dois. Alternatives and conditions are named, not inferred.

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