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togoid_convertId

Read-only

Convert biological identifiers across databases like NCBI Gene, UniProt, and PDB. Map IDs between source and target datasets, including multi-hop routes, with optional pagination.

Instructions

Convert identifiers from one database to another.

Maps IDs between biological databases — e.g., NCBI Gene IDs to UniProt accessions, or UniProt accessions to PDB structure IDs.

RETURNS a JSON string of a bare array of [source_id, target_id] pairs, e.g. '[["672", "P38398"], ["675", "O15129"]]'. An empty array means none of the input IDs converted along the route.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsYesSource IDs. Accepts either a list of strings (e.g., ["672", "675", "7157"]) or a comma-separated string ("672,675,7157"). Examples: "672,675,7157" (NCBI Gene IDs), "P38398,P04637" (UniProt)
limitNoMaximum number of results (default 10000)
routeYesComma-separated pair of dataset keys: 'source,target'. NOTE: this is a single joined string, NOT separate `source`/`target` args (as in countId/getRelation) — because a route may be multi-hop (3+ datasets). Examples: - 'ncbigene,uniprot' (Gene → Protein) - 'uniprot,pdb' (Protein → 3D Structure) - 'ncbigene,ensembl_gene' (NCBI Gene → Ensembl Gene) - 'chembl_target,uniprot' (Drug Target → Protein) - 'uniprot,chembl_target' (Protein → Drug Target) - 'ncbigene,hgnc' (Gene → HGNC symbol) Multi-hop routes are also supported: - 'ncbigene,uniprot,pdb' (Gene → Protein → Structure)
offsetNoPagination offset for large result sets

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral context by describing the return format (JSON string of [source_id, target_id] pairs) and the meaning of an empty array. It doesn't discuss failure modes or multi-hop edge cases, but it goes beyond the annotations.

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 two short paragraphs: the first states the purpose, the second explains the return format. It is front-loaded and every sentence contributes without verbosity. Excellent conciseness.

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 tool with 4 fully described parameters and an output schema, the description covers purpose, examples, return format, and empty-array semantics. It doesn't discuss error handling or limits beyond schema, but given the annotations and schema, it is quite complete.

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% for all 4 parameters, with detailed descriptions including examples. The description itself adds no extra parameter semantics beyond the schema; it merely repeats route examples already present in the schema. Baseline of 3 is appropriate.

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: converting identifiers between biological databases, with concrete examples like NCBI Gene to UniProt. This differentiates it from sibling tools like togoid_getRelation (which returns relations) and togoid_countId (which counts). The verb 'convert' plus the resource is specific.

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 implies usage for ID conversion through examples, but it does not explicitly mention when to use this versus alternatives like togoid_getRelation or togoid_countId. While the purpose is clear, there is no explicit 'when not to use' guidance, though the examples effectively illustrate typical use cases.

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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