studies_by_traitGWAS Catalog: find genome-wide association studies for a disease/trait (EFO trait, e.g. "asthma" or "type 2 diabetes"). Returns accession, PubMed ID, title, journal, date, sample size. Keyless, from EBI/NHGRI.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max studies to return (default 10). | |
| trait | Yes | Disease or trait name, e.g. "asthma", "type 2 diabetes". |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "trait": "asthma"
+ },
+ {
+ "limit": 25,
+ "trait": "type 2 diabetes"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds value by specifying return fields (accession, PubMed ID, etc.), that it is keyless, and the data source (EBI/NHGRI). This goes beyond annotations but does not cover all edge cases (e.g., pagination).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose. Every sentence adds value: first for what it does, second for return fields and source. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list-returning tool with good annotations and schema, the description is quite complete. It lists return fields, notes keyless access, and mentions the data source. The absence of pagination details is a minor gap, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters, so the baseline is 3. The description adds minimal extra meaning beyond the schema, only noting that trait is an EFO term. The schema already provides examples and descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds genome-wide association studies for a specific disease/trait, using the GWAS Catalog. It distinguishes from sibling tools like 'get_snp' and 'snp_associations' by focusing on studies by trait, and the name itself reinforces the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (to find studies for a given trait), but does not explicitly state when not to use it or mention alternative tools. However, the context is sufficient for an AI agent to infer appropriate usage.
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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