Best performance

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

Annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond the annotations: it explains how results are chosen ('largest sample, EUR-preferred') and what output formats are provided, which is genuine supplementary information.

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?

The description is compact and front-loaded: first sentence states the core action, second sentence summarizes the output. Every clause adds value and nothing is redundant.

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 single-parameter read-only lookup with a known output schema, the description provides necessary selection criteria and return-item summary. It lacks an explicit usage-guidance statement, but overall it is sufficient for this tool's complexity.

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?

The input schema only exposes pgs_id without any description, leaving 0% schema coverage. The tool description indirectly connects pgs_id to 'a score', which helps but does not define the identifier format or its origin. Since the parameter name is self-descriptive and the tool context implies polygenic score, this is adequate but minimal.

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's function: 'Look up the best evaluation performance for a score' and immediately clarifies the selection criteria ('largest sample, EUR-preferred'). It also names the returned data types, distinguishing it from sibling tools like search_scores or score_info.

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 implies when to use the tool—when parsed effect sizes and classification metrics are needed—but it does not explicitly state when to use this tool over alternatives. No exclusion conditions or alternative tool references are provided, making the guidance indirect rather than explicit.

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