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scitex-ai

crossref-local

by scitex-ai

enrich_dois

Retrieve full metadata for DOIs, including citation counts, references, volume, issue, and publisher. Use after search to expand basic paper details.

Instructions

Enrich DOIs with full metadata including citation counts and references.

Use this after search() to get detailed metadata for papers. The search() tool returns basic info (title, authors, year, journal). This tool adds: citation_count, references, volume, issue, publisher, etc.

Typical workflow:

  1. search("epilepsy seizure prediction") -> get DOIs

  2. enrich_dois([doi1, doi2, ...]) -> get full metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doisYesList of DOIs to enrich (e.g., ["10.1038/nature12373", "10.1126/science.aax0758"])

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains that the tool adds metadata fields like citation_count and references, which is useful, but it does not mention whether it performs network calls, validates DOIs, or has error conditions. It is not misleading, but it lacks depth about side effects and limitations.

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 well-structured and front-loaded with the main purpose. Each sentence serves a clear role: stating function, clarifying when to use, and giving a workflow example. It is concise without wasted words.

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?

The description provides a clear usage workflow and lists the enriched fields, and an output schema exists so return value details are not needed. It lacks only minor context about error handling or edge cases, but overall it is well-rounded for a simple enrichment tool.

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?

The schema already covers the single parameter 'dois' with a rich description and example. The tool description adds little beyond the schema's existing coverage, so the baseline of 3 is appropriate.

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 description clearly states the tool's purpose with a specific verb and resource: 'Enrich DOIs with full metadata including citation counts and references.' It also distinguishes itself from the sibling search() by explicitly listing what search() returns versus what this tool adds.

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 description gives explicit usage context: 'Use this after search()' and provides a typical workflow with steps. It also contrasts with the alternative search() tool, making it clear when to use this tool versus sibling tools.

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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