Get Training Stats

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

Annotations already mark the operation as read-only. Beyond that, the description discloses exact behavioral details: e1RM uses the same Epley formula as the app, statistics are app-consistent, and `trend.points` contains the full per-session series in addition to the first-vs-last delta. This is meaningful disclosure beyond the annotation.

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 dense and front-loaded, leading with the core purpose before elaborating on modes and output specifics. Every clause earns its place and there is no filler or repetition of the title.

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?

The description explains the output fields and the computational formula well, but the gap between the described `by` parameter and the empty input schema is significant: an agent cannot tell how to request exercise-specific or muscle-specific stats. There is also no output schema, so the description partially compensates by naming key fields, but the invocation mismatch remains unresolved.

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?

There are zero parameters in the input schema, so the baseline is 4. The description adds useful conceptual meaning by explaining the `by` modes and what they return. However, it references a `by` argument that is absent from the input schema, which is a minor inconsistency in how an agent would actually invoke the tool.

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 opens with a specific verb ('Compute') and a specific resource ('training statistics'), then names concrete outputs (e1RM, volume, set/session counts, trend). It also distinguishes three modes of operation (`by: 'exercise'`, `by: 'muscle'`, `by: 'capabilities'`), making it easy to tell apart from siblings like get_progress or get_history.

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 gives clear conditional guidance on when each mode applies: a specific exercise, a muscle group, or a capabilities breakdown. It does not explicitly name alternative sibling tools or state when not to use this tool, but the usage context is unmistakable.

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