training_plan

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

Beyond the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false), the description discloses specific behavioral details: the queue is capped at 50 plans, save returns the stored plan with id, verify returns per-target means and all_met, and done optionally links a result_session_id. It also clarifies that verify grades 'a recent session's real shot metrics,' which is not in the schema. No contradictions with annotations.

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 for a five-action tool, with a clear one-line summary followed by a bulleted list. Each action gets a compact, information-dense line. There is no filler or repetition; every sentence adds value.

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?

Covers all 6 parameters, all 5 actions, return types, state changes, and constraints (cap of 50). It explains the purpose of each action in context and references output behaviors. The existing output schema can further detail return structures, but the description alone is sufficient for an agent to select and invoke the tool correctly for most scenarios.

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 coverage is 0%, so the description carries full weight—and it succeeds. It defines the 'plan' parameter as a structured dict with fields (title, focus, diagnosis, blocks, target_specs), gives an example target_specs shape, explains 'needs plan_id' for done and verify, specifies status enum values, and clarifies the meaning of activity_id and result_session_id. This adds profound meaning beyond the bare 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 opens with 'The coach's memory: save prescribed practice plans and recall/grade them,' which clearly states the tool's purpose and scope. It then enumerates five specific actions (save, next, list, done, verify) with distinct verbs and resources, distinguishing itself from sibling tools like session_analysis and trackman.

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?

Each action includes contextual usage: save persists to a pending queue, next answers 'what's today's training?', list shows plans oldest→newest, done marks complete, verify grades against target_specs. However, the description does not explicitly mention when to avoid this tool or use an alternative, so it misses full alternative/exclusion guidance.

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