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togoid_getRelation

Verify if a specific ID conversion route exists between two biological databases and retrieve the relationship details.

Instructions

Check if a specific ID conversion route exists and get its details.

Use this to verify that a particular source→target conversion is available before calling convertId. Also reveals the nature of the relationship (e.g., "encoded by", "has structure", "is target of").

Args: source: Source database key (e.g., 'uniprot', 'ncbigene', 'chembl_target') target: Target database key (e.g., 'pdb', 'ensembl_gene', 'hgnc')

Returns: List of relationship objects with: - forward: relationship label from source to target - reverse: relationship label from target to source - description: explanation of the link

Example: >>> getRelation('ncbigene', 'uniprot') # Shows: ncbigene → uniprot via "encoded by" relationship

>>> getRelation('uniprot', 'pdb')
# Shows: uniprot → pdb via "has structure" relationship

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes
targetYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the tool as a read-like operation (checking and returning details) and specifies the return format, but does not explicitly state it is non-destructive or mention any side effects, rate limits, or permissions. An explicit statement of read-only behavior would improve this.

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 well-structured with a summary, usage note, Args, Returns, and examples. Every sentence adds value, and there is no redundancy or fluff. It is 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.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 required parameters and an output schema exists, the description provides complete information: what it does, how to use it, and what it returns. It could optionally mention behavior when a route does not exist, but it is sufficiently complete for an AI agent to use correctly.

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?

The input schema has 0% description coverage, making the description the sole source of parameter meaning. The description provides clear parameter names and examples (e.g., 'uniprot', 'ncbigene') in the Args section, adding significant value beyond the schema. However, it does not list all possible values or types explicitly.

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 checks if a specific ID conversion route exists and gets its details. It uses specific verbs and resource, and distinguishes itself from the sibling tool `togoid_convertId` by advising use before calling that tool.

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 explicitly advises using this tool before calling `convertId` to verify availability, providing clear when-to-use guidance. It does not explicitly state when not to use, but the context implies alternatives (e.g., `convertId` for actual conversion).

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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