get_height

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals important traits: the season parameter defaults to the current season, earliest available data is 2007, and continuity data starts from 2008. It also indicates the return format as a JSON array, providing useful context beyond just the tool's name.

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 well-structured and front-loaded with the core purpose in the first sentence. It avoids redundancy by using concise sections for Args and Returns, and every sentence contributes to understanding the tool. No filler or unnecessary detail is present.

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 optional parameter and an existing output schema, the description is largely complete. It explains the parameter's behavior, default, and data availability, and gives a high-level return type. It could be slightly more comprehensive by explaining what 'continuity metrics' entails, but the output schema likely covers that.

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 schema only specifies 'season' as a nullable string with a null default, providing no meaning. The description compensates fully by explaining the parameter's format ('e.g., "2024"'), default behavior, and historical data boundaries. This adds significant semantic value beyond the schema.

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 with a specific verb ('Get') and resource ('height and experience data for all teams'). It distinguishes from sibling tools by enumerating unique metrics like average height, experience, bench minutes, and continuity metrics, making its purpose unambiguous.

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 usage context by detailing what data is returned, but it does not explicitly state when to use this tool over alternatives or provide exclusions. The sibling list offers context, yet no direct comparison is made, leaving usage guidance to inference.

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