Catalogueoflife

Vernacular

vernacular

Read-onlyIdempotent

"What's the common name for [Latin binomial]" / "what do people call [scientific name]" / "vernacular names of [species]" — list common/vernacular names (in various languages) for a Catalogue of Life taxon. Use to map "Panthera leo" → "lion" / "leon" / "lwów" etc.

Input Schema

Name	Required	Description	Default
`id`	Yes
`dataset`	No

Output Schema

Name	Required	Description	Default
`count`	Yes	Number of items returned.
`items`	Yes	Vernacular (common) names for a taxon

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedMay 27, 2026

addedInput schema / examples

Added value: +[
+  {
+    "id": "7XCCF"
+  }
+]

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "count": {
+      "description": "Number of items returned.",
+      "type": "integer"
+    },
+    "items": {
+      "description": "Vernacular (common) names for a taxon",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "items",
+    "count"
+  ],
+  "type": "object"
+}

First observedMay 14, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds context about multilingual name lists and the Catalogue of Life source, but does not disclose error behavior or limitations. No contradiction with annotations.

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?

The description is concise and front-loaded with example queries, conveying the tool's purpose and usage in one sentence. It avoids unnecessary repetition and structure is clean.

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?

Given the simple nature of the tool, the presence of an output schema, and strong annotations, the description is reasonably complete. It explains the purpose, gives examples, and identifies the data source. The main gap is parameter documentation, but that falls under parameter semantics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, and the description does not explain the 'id' or 'dataset' parameters. Although it mentions 'Catalogue of Life taxon', it doesn't explicitly connect this to the required id parameter or provide format guidance, leaving parameter meaning largely unexplained.

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 specifies the action ('list') and resource ('common/vernacular names for a Catalogue of Life taxon'), with concrete examples such as mapping 'Panthera leo' to 'lion'/'leon'/'lwów'. This clearly distinguishes it from related scientific-name tools like synonyms.

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 includes an explicit use case ('Use to map...') and provides natural language query examples. However, it does not explicitly state when not to use this tool or directly compare it to alternatives like synonyms, leaving some room for ambiguity.

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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