Statistics for a racetrack

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 discloses a requirement ('Requires the statistics product') and lists the types of statistics returned (field sizes, distances, post position impact). It does not explicitly state side effects (though it is clearly a read-only operation), auth beyond product, rate limits, or error behavior, leaving some transparency gaps. It does add value beyond the schema by describing what the statistics cover.

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 two sentences, front-loaded with the core purpose, and concludes with the product requirement. Every word adds value; no fluff or redundancy. It is appropriately concise for the tool's simplicity.

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 tool with one parameter and no output schema, the description is fairly complete: it states what it returns, the prerequisite, and the scope. It does not describe output format or error conditions, but these are less critical for a simple read-only statistics tool. Given the absence of annotations, it could be more explicit about being non-mutating, but the term 'Statistics' implies read-only. Overall, it meets the needs for selection and invocation.

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 description coverage is 100% for the single parameter (track_code with example 'SOL' or 'AX'). The description adds no extra meaning about the parameter beyond what the schema provides (e.g., 'one Nordic track' is redundant). Since coverage is high, this scores at the baseline 3 without additional clarification.

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 purpose: 'Statistics for one Nordic track: typical field sizes, distances raced and how post position affects winning there.' It specifies the resource (one Nordic track) and the type of statistics provided, distinguishing it from sibling tools like get_horse_statistics or get_driver_statistics. The verb is implied by the noun 'Statistics', but the focus is clear.

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 an implicit usage context: use this tool when you need track-level statistics for a Nordic track. However, it does not explicitly contrast with alternatives (e.g., 'for horse-level statistics, use get_horse_statistics') or state exclusions. It notes a prerequisite ('Requires the statistics product') but does not guide when to choose this tool over siblings.

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