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gbif_occurrence_count

Read-onlyIdempotent

Count GBIF occurrences matching filters like taxon, country, year, or geometry without downloading full records. Validate filters and gather statistics for dashboards before running full searches.

Instructions

Fast endpoint for counting occurrences matching filters without retrieving full records. Useful for statistics, dashboards, and filter validation before running full searches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text search query
yearNoYear or year range (e.g., "2020,2024")
issueNoData quality issues
monthNoMonth (1-12)
countryNoISO country code
classKeyNoClass taxon key
genusKeyNoGenus taxon key
geometryNoWKT geometry
orderKeyNoOrder taxon key
taxonKeyNoGBIF taxon key
continentNoContinent filter
familyKeyNoFamily taxon key
phylumKeyNoPhylum taxon key
datasetKeyNoDataset UUID
kingdomKeyNoKingdom taxon key
basisOfRecordNoBasis of record types
hasCoordinateNoFilter to records with coordinates
publishingOrgNoPublishing organization UUID
scientificNameNoScientific name to search for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior; the description adds the 'fast' performance trait and that no full records are returned, but says nothing about return shape (single total vs. breakdown) or throttling limits.

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 tight sentences, front-loaded with the fast/no-records distinction, and every phrase (statistics, dashboards, filter validation) adds real usage signal with no filler.

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 read-only count endpoint with fully documented params, the description is largely sufficient; the main missing piece is whether the result is a single number and how multiple filters combine (AND semantics).

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?

Schema coverage is 100% with every one of the 19 filter params documented inline, so the schema carries the parameter burden. The description only characterizes the filters collectively, adding no syntax or semantics beyond the schema. Baseline 3 applies.

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?

States a specific verb (counting) and resource (occurrences) with the key differentiator 'without retrieving full records,' which cleanly separates it from gbif_occurrence_search and the counts_by_* breakdown siblings.

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 clear usage contexts (statistics, dashboards, filter validation before full searches) and implicitly positions it ahead of full searches, but does not explicitly name the sibling to use when breakdowns by dimension are wanted.

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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