workbench

Find orthologs/paralogs for gene symbols across species

ortholog_map

Read-onlyIdempotent

Look up the orthologous (or paralogous) gene for up to 50 gene symbols in a target species, via Ensembl's homology-by-symbol REST endpoint. Symbols with no homology record are reported in unmapped, never silently dropped.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`type`	No	Homology type to return. Defaults to orthologues.	orthologues
`symbols`	Yes	Gene symbols to look up, up to 50 (e.g. ["TP53", "BRCA1"]).
`sourceSpecies`	No	Ensembl species slug the symbols belong to (e.g. "human", "mouse"). Defaults to "human".	human
`targetSpecies`	Yes	Ensembl species slug to find homologs in (e.g. "mouse", "rat", "zebrafish", "fruit_fly").

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint and idempotentHint, so the agent knows it is safe and idempotent. The description adds that unmapped symbols are reported in 'unmapped' and never silently dropped, which is valuable beyond the annotations. No contradictions.

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 extremely concise with only two sentences, front-loaded with the key verb 'Look up'. Every sentence provides essential information without redundancy.

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?

Given that there is no output schema and the tool is a lookup, the description hints at the output structure by mentioning the 'unmapped' field. However, it does not fully describe the response format for successful mappings, which could be improved. Still, it is fairly complete for the complexity level.

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 coverage is 100%, so parameters are well-documented. The description adds context about the 50-symbol limit and the behavior for unmapped results, which goes beyond the schema. It also explicitly mentions the Ensembl REST endpoint, adding operational context.

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 tool's purpose: looking up orthologous/paralogous genes for up to 50 gene symbols across species via Ensembl's homology-by-symbol endpoint. It uses a specific verb and resource, and distinguishes itself from sibling tools by its focused functionality.

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?

The description implicitly guides when to use this tool (when needing orthologs/paralogs for gene symbols) and mentions the source endpoint and constraints. No explicit exclusions or alternatives are given, but the context is clear enough for selection.

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

A3.6/5.0

Disambiguation4/5

Most tools have highly specific purposes (e.g., crispr_grna_design vs base_editing_design vs prime_editing_design). However, there is some overlap in sequence analysis tools (characterize_sequence, sequence_report) and plasmid annotation tools (plasmid_annotate vs plasmid_deep_annotate) which could cause confusion.

Naming Consistency3/5

The naming pattern is largely consistent with snake_case verb_noun or noun_descriptor (e.g., primer_design, plasmid_annotate, fastq_trim). However, there are exceptions like 'batch', 'workflow', 'gc_content', and 'cloning_diagnose' which don't follow the verb_noun pattern consistently. Also, some names are phrases like 'golden_gate_from_parts'.

Tool Count2/5

With 101 tools, this server is extremely large and likely overwhelming for agents. Even for a comprehensive bioinformatics toolkit, this exceeds a manageable scope, risking agent confusion and inefficient tool selection. A more modular approach would be advisable.

Completeness4/5

The tool surface covers a wide range of bioinformatics workflows including sequence analysis, primer design, cloning, CRISPR, NGS, expression analysis, and data export. There are minor gaps such as lack of a dedicated protein structure prediction tool and limited off-target genome coverage, but overall the set is impressively complete for its domain.

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