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jean — Molecular Biology & Genomics

lookup_ontology

Read-only

Resolve an ontology term, pathway or cell type: OLS ontologies, QuickGO annotations, Reactome pathways, KEGG entries, Rfam RNA families, CellGuide/PanglaoDB cell types and markers, or an antibody RRID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNomaximum records (1–20).
queryYesa term, accession or command such as "get_go_annotations:P04637" or "map_reactome_pathways:TP53".
_filesNoThe project: record references shaped like _subject. Their records become library documents a tool names in sequences/parts/document; a trace or alignment is found by file name.
sourceYeswhich authority to ask.
_subjectNoThe record to act on: exactly one of sequence, accession (+source), sample (an id from list_samples) or file ({name, text} or {name, base64}); record_index picks one record of several.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYeshow many records came back, which is a result even when it is zero
recordsYesthe records the authority returned, in its own field names — wire them into a tool that takes ids, or read them in the transcript
envelopeYesthe authority's whole answer: paging, totals and the query it actually ran
provenanceYesthe authority, the exact endpoints called and when — what makes an answer re-checkable without re-running it

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so the safety and network profile is covered. The description adds that it queries named external authorities, which reinforces the open-world nature, but says nothing about rate limits, caching, failure modes, or how results from multiple authorities are merged.

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?

A single front-loaded sentence with the verb first and no filler. The authority list is long but each entry maps to a real source enum value, so it earns its place rather than padding.

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?

With an output schema present, return values need not be explained, and all five parameters are schema-documented. What remains thin is the decision guidance for choosing between this tool and the other lookup_* siblings, which is the only substantive gap for a five-parameter, nested-object tool.

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 description coverage is 100%, so the enum values for 'source' and the command-style syntax for 'query' are already documented in the schema; the description's list of authorities largely restates the enum. No additional meaning is added for 'limit' or the nested _subject/_files objects, so the 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?

The description opens with a specific verb ('Resolve') and a concrete resource set (ontology term, pathway, cell type, antibody RRID), then enumerates the eight authorities it covers. That scope makes it distinguishable from sibling lookups such as lookup_gene, lookup_protein, or lookup_compound, though it never names those siblings explicitly to draw the boundary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: by listing OLS, QuickGO, Reactome, KEGG, Rfam, CellGuide/PanglaoDB and Antibody Registry, an agent can infer when this tool applies, but there is no explicit when-to-use/when-not statement and no named alternative for adjacent tasks (e.g. gene or protein lookup).

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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