get_activity_power_hr

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states that values are source-provided, that no new physiological calculations or causal conclusions are made, and that compact mode bounds data to 32 KiB by omitting whole fields. It also explains that exact omissions and a continuation are returned. This is strong transparency for a read-only tool, though it does not address auth or rate-limit behavior.

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 appropriately sized and front-loaded with the core purpose. Every sentence adds useful information, and the technical details about data bounding and continuation are compact rather than rambling. No unnecessary filler 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?

An output schema exists, so return-value details are not required. The description covers the key behavioral nuances: compact vs. full detail, data-size bounding, omission behavior, and continuation. Missing guidance on when to prefer this over sibling analysis tools and the lack of api_key semantics are the main gaps, but overall the description is reasonably complete for a read-only analysis endpoint.

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 0%, so the description must compensate. It adds real meaning to the detail parameter by contrasting compact behavior (first 120 series rows, eight curves, 32 KiB bound) against full detail (complete JSON object). However, it does not explain the api_key parameter, and activity_id is only inferable from its name. The compensation is partial, not complete.

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 starts with a specific verb and resource: 'Read native power-versus-HR analysis, including upstream HR lag and windows.' This clearly identifies the tool's domain and separates it from sibling tools like get_activity_power_curves, which focus on power curves rather than power-versus-HR analysis. It also clarifies the source-provided nature of the values.

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?

No guidance is given about when to use this tool versus sibling alternatives like get_activity_streams or get_activity_details. The compact/full detail explanation addresses output size, not tool selection. The agent is left to infer the appropriate context entirely from the tool name.

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