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Search Datasets

search_datasets
Read-onlyIdempotent

"Find research datasets about [topic]" / "open data on [subject]" / "look up [field] datasets" — search research datasets registered in OpenAIRE (Zenodo, Dryad, Figshare, B2SHARE etc.). Use for finding open research data to reuse / cite.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo1-based page (default 1)
sizeNoPage size, 1-100 (default 20)
queryYesFree-text query (matches title/abstract/keywords)
to_yearNo
from_yearNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYesCurrent page number (1-based)
countYesNumber of results in this response
queryYesThe search query that was executed
totalYesTotal number of matching datasets
resultsYesArray of dataset objects
page_sizeYesNumber of results per page

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "query": "genomic sequencing data"
      +  },
      +  {
      +    "from_year": "2015",
      +    "query": "climate observations",
      +    "to_year": "2024"
      +  }
      +]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "count": {
      +      "description": "Number of results in this response",
      +      "type": "number"
      +    },
      +    "page": {
      +      "description": "Current page number (1-based)",
      +      "type": [
      +        "number",
      +        "null"
      +      ]
      +    },
      +    "page_size": {
      +      "description": "Number of results per page",
      +      "type": [
      +        "number",
      +        "null"
      +      ]
      +    },
      +    "query": {
      +      "description": "The search query that was executed",
      +      "type": "string"
      +    },
      +    "results": {
      +      "description": "Array of dataset objects",
      +      "items": {
      +        "type": "object"
      +      },
      +      "type": "array"
      +    },
      +    "total": {
      +      "description": "Total number of matching datasets",
      +      "type": [
      +        "number",
      +        "null"
      +      ]
      +    }
      +  },
      +  "required": [
      +    "query",
      +    "total",
      +    "page",
      +    "page_size",
      +    "count",
      +    "results"
      +  ],
      +  "type": "object"
      +}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds context about the aggregated sources and purpose, which complements the annotations without contradiction.

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?

Two sentences plus example queries, front-loaded with purpose and examples. Efficient and no wasted words.

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?

With an output schema available, the description is complete for a search tool. It explains the scope and use case sufficiently.

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 coverage is 60%; description adds value through examples that illustrate usage of from_year and to_year, though it does not provide detailed descriptions for these parameters.

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 'Find research datasets' with example queries and specifies the sources (OpenAIRE, Zenodo, Dryad, Figshare, B2SHARE). It distinguishes from sibling tools like search_publications and search_software.

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 description provides example queries and states 'Use for finding open research data to reuse / cite.' While it implies when to use, it doesn't explicitly list alternatives or when not to use, but the context is clear.

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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