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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is fully covered and the description adds the meaningful scoping detail that the conversation/feedback data is limited to the last 30 days. It does not disclose auth requirements, rate limits, or the shape of the aggregated response, so it is useful context rather than rich behavioral disclosure.

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?

A single front-loaded sentence led by 'Training at a glance', with the payload enumeration following. It is dense but every clause names a distinct returned data category, so little is wasted, though the comma-list style is slightly harder to scan than bullet-style structure would be.

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?

With no output schema, the description carries the burden of explaining what comes back and does so by naming each returned section (conversations, AI resolutions, feedback, playbook rules, eval runs, eval runnability). An agent knows what to expect. It is only missing granularity about the aggregate's shape or any pagination/limits beyond the 30-day window.

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 100% and the single productId parameter is already documented as scoping playbook and evals. The description's '(per product)' phrasing echoes the same idea without adding format, default, or behavior when omitted, so the schema is doing the heavy lifting and the baseline 3 applies.

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?

States a specific verb and resource ('Get the training overview') and then enumerates the concrete contents returned: 30-day conversations/AI resolutions, answer feedback, playbook rules, eval runs and eval availability. That is enough for an agent to know it is a read-only dashboard-style aggregate, distinct from detail tools like get_agent or run_agent_evaluation. It stops short of explicitly naming a sibling it is not, so it is clear but not fully differentiated.

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?

'Training at a glance' faintly implies a summary use case, but there is no statement of when to pick this over alternatives such as get_agent, get_ai_settings, list_training_proposals or run_agent_evaluation, and no prerequisites or exclusions. The agent is left to infer the routing decision entirely from the resource name.

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