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gbif-biodiversity-mcp-server

by io.github.cyanheads

Bulk Match Species Names

gbif_bulk_match_species
Read-onlyIdempotent

Resolve up to 50 scientific names to GBIF backbone taxon keys in one call — the batch counterpart to gbif_match_species for checklist, inventory, and species-list workflows that would otherwise need one round trip per name. Each name is matched independently and results are returned in input order, one entry per name. A name with no backbone match yields matchType NONE (no taxonKey) instead of failing the batch; a per-name lookup failure yields matchType ERROR carrying that name's error message and, when the failure was classified, a machine-readable reason — the rest of the batch is unaffected, and the call as a whole still succeeds. When a queried name is a synonym, taxonKey is the accepted taxon it resolves to and matchedTaxonKey carries the synonym's own key. Common names are not supported — use gbif_search_species for vernacular searches. Below confidence 80, review the match.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesYesScientific names to match against the GBIF backbone. 1–50 per call, matched in parallel.
strictNoWhen true, require an exact match for every name (no fuzzy matching). When false (default), GBIF applies fuzzy matching to tolerate minor misspellings.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
resultsNoOne result per input name, in input order.

TDQS

A4.7/5.0
Behavior5/5

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

The description goes far beyond the annotations (readOnly, openWorld, idempotent) to explain per-name independent matching, input-order results, NONE for no match, ERROR for per-name failures, batch-level success despite individual failures, synonym handling, and the semantics of taxonKey vs. matchedTaxonKey. This matches the annotations and adds essential behavior not otherwise conveyed.

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 core purpose, then expands into behavior and exclusions. Every sentence adds value—covering batch capability, result ordering, error handling, synonym resolution, unsupported names, and confidence guidance—without redundancy or fluff.

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?

Given the tool's complexity (batch handling, per-name outcomes, error types, synonym behavior) and the existence of a rich output schema, the description covers all critical aspects an agent needs to call it correctly and interpret results. The presence of an output schema means return values don't need to be re-explained, and the description fills in behavioral nuances comprehensively.

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% for both parameters (names and strict), with detailed descriptions including defaults and behavior. The tool description adds no additional parameter-specific semantics beyond what the schema already provides, so it correctly stays at the baseline of 3.

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 verb 'resolve', the resource 'scientific names to GBIF backbone taxon keys', and explicitly distinguishes it as the batch counterpart to gbif_match_species. It also specifies the target workflows (checklist, inventory, species-list), making its purpose unambiguous and distinct from siblings.

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?

It explicitly states when to use the tool (batch workflows needing multiple names) and provides a clear exclusion: common names are not supported, with the alternative gbif_search_species recommended. It also gives a confidence threshold (`Below confidence 80, review the match`) which guides post-call actions.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool targets a distinct action and resource pair: matching (single and batch), retrieving species details, hierarchy traversal, searching occurrences, counting, aggregating by facets, and searching datasets/publishers. There is no functional overlap or ambiguity between tools.

Naming Consistency4/5

The naming is largely consistent with the gbif_ prefix followed by a verb and noun (e.g., gbif_match_species, gbif_count_occurrences, gbif_get_dataset). However, a few tools use a search_<noun> pattern (e.g., gbif_search_datasets) and one uses a noun phrase without a verb (gbif_occurrence_facets), introducing a minor inconsistency.

Tool Count5/5

13 tools is well within the optimal range for a domain-specific API. The tools cover taxonomy resolution, species lookup and hierarchy, occurrence search/count/aggregation, and dataset/publisher discovery—no unnecessary bloat or sparse coverage.

Completeness4/5

The surface covers the core GBIF workflows: match → search/get occurrences, get species details and classification, and find datasets/publishers. Missing bulk download is explicitly acknowledged and not served by this tool, and searching datasets by taxon is indirect (via occurrence datasetKey), but these are workable gaps rather than blockers.

Resources