Skip to main content
Glama

togoid_convertId

Convert biological database identifiers by specifying a source-target route, e.g., ncbigene to uniprot.

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: JSON string: a bare array 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
limitNo
routeYes
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as side effects, authentication requirements, rate limits, or error conditions. It only mentions the return format and default limit, which is insufficient for a tool with no annotations.

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?

The description is well-structured with sections for purpose, workflow, args, returns, and use cases. It is slightly verbose but every sentence adds value; could be more concise without losing information.

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 tool's complexity (4 parameters, no annotations, output schema exists), the description covers inputs, output format, workflow, common use cases, and examples, making it nearly complete for an AI agent to use.

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 input schema has 0% description coverage, but the description compensates thoroughly by explaining each parameter: ids accepts list or comma-separated string with examples, route provides numerous examples of valid route strings, and limit/offset are explained with defaults.

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 it converts identifiers between biological databases, providing specific examples (e.g., NCBI Gene to UniProt) and distinguishing it from sibling tools like togoid_countId and togoid_getAllRelation via the workflow guidance.

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 includes an 'IMPORTANT WORKFLOW' section that tells the user to call getAllRelation/getRelation and countId before using this tool, but it does not explicitly state when not to use it or describe alternatives beyond the workflow.

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