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

The description goes beyond the readOnly and idempotent annotations by explaining that the tool recommends fresh muscles and how include_bodyweight=true affects recovery calculations. It does not contradict the annotations and adds useful behavioral context.

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 dense sentences cover purpose, use case, and parameter behavior with no filler. The most important information is front-loaded, making it easy to scan.

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?

Given the output schema is present, the description does not need to explain return values. It covers what the tool does, when to use it, and how the optional parameter modifies behavior, making it complete for this read-only tool.

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?

While the input schema already describes include_bodyweight, the description adds meaningful context: it explains why the parameter exists (to make recovery reflect real load) and implies it only applies to bodyweight exercises. This adds value beyond the schema's field description.

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 returns per-muscle 7-day recovery percentages and explicitly positions it as the 'what should I train today?' input, recommending fresh muscles. This distinguishes it from sibling tools like list_muscles and get_body_metrics by focusing on recovery-based training recommendations.

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 phrase 'the what should I train today? input' provides clear context for when to use this tool. It does not explicitly exclude alternatives or name sibling tools, but the intended use case is unambiguous.

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