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smaniches
by smaniches

Disease Target Evidence

get_disease_targets
Read-onlyIdempotent

Identify top protein targets for a disease using evidence scores from genetic association, somatic mutations, known drugs, pathways, literature, animal models, and RNA expression.

Instructions

Return top protein targets for a disease with Open Targets evidence scores.

Evidence score breakdown (0–1 per data type):

  • genetic_association: GWAS + rare-variant signals

  • somatic_mutation: Cancer somatic variant evidence

  • known_drug: Approved or clinical-stage drugs

  • affected_pathway: Pathway membership (Reactome, SIGNOR)

  • literature: Text-mining evidence (Europe PMC)

  • animal_model: Knockout / model organism phenotypes

  • rna_expression: Differential expression evidence

Example: get_disease_targets(disease_id='MONDO:0007254', limit=15) returns top 15 targets for breast carcinoma.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, and openWorldHint. The description adds value by detailing the evidence score breakdown (0–1 per data type) and an example call. No contradiction with annotations. It does not cover limits or error behavior, but for a read-only query tool this is adequate.

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 relatively concise and front-loaded with the main purpose. It includes an example and a structured list of evidence scores. Slightly verbose with the score breakdown, but still clear and easy to scan.

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 presence of an output schema, the description doesn't need to detail return values. It explains the purpose, provides an example, and lists evidence types. For a tool with one required parameter, it covers essential context sufficiently.

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?

The input schema has 0% description coverage per context, so the description must compensate. It mentions 'disease_id' and 'limit' in the example but does not explicitly describe 'min_score' or 'include_tractable_only'. The evidence score list is about output, not parameters. More parameter explanation in the description would be beneficial.

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 'Return top protein targets for a disease with Open Targets evidence scores.' It specifies the action (return targets), resource (Open Targets), and provides detailed evidence score breakdown. It distinguishes itself from siblings like 'get_common_disease_targets' by focusing on targets per disease with scoring.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description lacks when-to-use, when-not-to-use, or comparison with sibling tools such as 'search_diseases' or 'get_target_diseases'. The example is helpful but not sufficient for contextual decision-making.

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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