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ensembl_homology

Read-only

Find orthologues or paralogues across species via Ensembl Compara. Input a gene symbol or ID to get homologs with percent identity, optionally filtered by species or taxon.

Instructions

Find orthologues or paralogues of a gene across species (Ensembl Compara) — the cross-species gene mapping source in biomcp.

Returns target stable IDs, species, taxonomy level, and percent identity, sorted by identity. Accepts an HGNC symbol or Ensembl gene ID; scope to one species with target_species ('mouse') or target_taxon (10090). Use it for conservation questions ('is this gene conserved?', 'what is the mouse orthologue of BRAF?').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geneYesGene symbol (BRAF) or Ensembl gene ID (ENSG00000157764)
typeNoHomology type to fetchorthologues
limitNoMaximum homologies to return (sorted by percent identity)
speciesNoSource species — 'human' (default), 'mouse', …human
target_taxonNoRestrict results to a taxon ID, e.g. 10090 (Mus musculus)
target_speciesNoRestrict results to one species, e.g. 'mouse'
Install Server

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, and the description adds useful behavioral detail: returns target stable IDs, species, taxonomy level, and percent identity, sorted by identity. This goes beyond the structured annotations by describing the result shape and ordering without contradicting the read-only hint.

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 compact and well-structured: the first sentence front-loads purpose and domain, the second covers return values and scoping, and the third gives user intent. Every sentence earns its place with no filler.

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?

There is no output schema, but the description supplies return fields and sort order, which covers what an agent needs to interpret results. The required gene parameter and key optional scope parameters are explained with examples, making the tool complete for a read-only cross-species lookup.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining accepted gene input formats (HGNC symbol vs Ensembl gene ID) and by giving concrete examples for target_species ('mouse') and target_taxon (10090).

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 starts with a specific verb and resource: 'Find orthologues or paralogues of a gene across species (Ensembl Compara)'. It also explicitly labels itself 'the cros-species gene mapping source in biomcp', which clearly distinguishes it from gene_search, ensembl_lookup, and other sibling tools.

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?

It gives explicit use-case guidance: conservation questions and the example 'what is the mouse orthologue of BRAF?'. It does not name alternative tools to avoid, but the cross-species positioning and usage examples make the appropriate context clear.

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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