Skip to main content
Glama

togoid_convertId

Maps identifiers between biological databases. Specify source and target datasets to convert IDs like NCBI Gene to UniProt or UniProt to PDB.

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.

IMPORTANT WORKFLOW: 1. First call getAllRelation() or getRelation() to verify the conversion route exists 2. Optionally call countId() to check how many IDs will convert 3. Then call convertId() with your IDs

Args: ids: Source 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) route: Comma-separated pair of dataset keys: 'source,target'. 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) limit: Maximum number of results (default 10000) offset: Pagination offset for large result sets

Returns: List of [source_id, target_id] pairs. Example: [["672", "P38398"], ["675", "O15129"], ...]

Common use cases: - Bridging databases on different SPARQL endpoints - Mapping gene IDs to protein accessions for UniProt SPARQL queries - Finding PDB structures for a set of proteins - Identifying ChEMBL drug targets for a list of genes

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsYes
routeYes
limitNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It explains the return format and supports multi-hop routes, but does not disclose potential side effects, rate limits, or authorization needs. Still, it covers essential behavioral aspects well.

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?

Well-structured with sections (workflow, args, returns, use cases). Every sentence is informative and not redundant. Front-loaded with core purpose, then logically flows to details. Appropriately sized for the tool's complexity.

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 complexity of ID conversion and the presence of sibling tools, the description is comprehensive. It covers prerequisites, parameter usage, return format, and typical scenarios. The workflow instructions ensure correct invocation, and the output schema is implicitly described.

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

Parameters5/5

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

The schema has no descriptions, but this description fully explains each parameter: acceptable formats for ids (list or comma-separated), route examples with database pairs, and default values for limit/offset. Schema coverage is 0%, and the description completely compensates.

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 converts identifiers between databases (e.g., NCBI Gene IDs to UniProt), with specific examples. It distinguishes itself from siblings like togoid_countId and togoid_getRelation by focusing on the conversion action.

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?

Provides a detailed workflow: first call getAllRelation/getRelation, then optionally countId, then convertId. Includes route examples, multi-hop routes, and common use cases, making when-to-use and when-not-to-use clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dbcls/togomcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server