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gbif_species_parse_names

Read-onlyIdempotent

Parse and standardize up to 1,000 scientific names in batch, extracting genus, species, authorship, rank, and quality indicators for data cleaning.

Instructions

Parse and standardize scientific names in batch using GBIF's name parser. Extracts genus, species, authorship, rank, and more from scientific name strings. Essential for data cleaning, quality checking, and standardizing names from spreadsheets or databases. Handles up to 1000 names per request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesYesArray of scientific names to parse. Each name is a string that may include authorship, year, and infraspecific epithets. Examples: ["Panthera leo (Linnaeus, 1758)", "Puma concolor", "Quercus robur L."]. Maximum: 1000 names per request. Returns parsed components for each name including: genus, specificEpithet, scientificName, authorship, year, rank, and parsing quality indicators.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, open-world, and non-destructive, so the safety profile is covered. The description's only behavioral addition is the 1000-name batch cap, which is duplicated in the schema, and it says nothing about how unparseable names are reported or what the quality indicators mean. Modest added value.

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?

Three tightly packed sentences: what it does, what it extracts, why it matters, and the size limit. Front-loaded and free of redundancy, though 'Essential for...' is mildly promotional rather than informational.

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 single-parameter read-only parser with no output schema, the description supplies the batch limit, the extracted components, and the use context; the schema's parameter text also enumerates returned fields. Missing only failure/quality behavior for unparseable names, which is a minor gap.

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?

There is a single parameter with 100% schema description coverage, including format examples and a list of returned components, so the schema carries the load. The description only restates the batch limit and extracted fields rather than adding new semantics. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Parse and standardize scientific names') and adds scope ('in batch') plus output fields (genus, species, authorship, rank). However, it never distinguishes itself from close siblings like gbif_species_match, gbif_species_suggest, or gbif_species_search, which an agent could plausibly confuse with name parsing.

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?

Gives concrete contexts for use: data cleaning, quality checking, and standardizing names from spreadsheets or databases. It stops short of naming alternatives or stating when NOT to use it (e.g., single-name lookups should go to gbif_species_match), so it is clear context without exclusions.

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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