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

Beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description discloses error handling behavior ('on bad input the error lists the valid ids — re-ask rather than guessing') and specifies the return content ('suggested focus areas plus the recommended package with real prices'). It also notes that routing matches the website, adding useful context. Since no output schema is present, this disclosure is important and adequately conveyed.

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 concise, consisting of two sentences plus a next-step clause. It front-loads the core purpose and includes actionable error-handling guidance. The phrasing is efficient without excessive wording, and it flows logically from purpose to error handling to the recommended follow-up. No redundant or filler content 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?

Given the absence of an output schema, the description adequately explains what the tool returns (focus areas and package with real prices) and how it behaves on bad input. It also places the tool within a broader workflow by suggesting a next action. However, it does not explain how to obtain the required role_band and motivation ids (though the schema references get_mentoring_options) nor does it mention the influence of optional parameters like audience or leaders_count. These are covered in the schema, so the description is largely complete but could be more explicit about dependencies.

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?

The input schema already provides complete descriptions for all five parameters (100% coverage), so the baseline is 3. The description mentions role_band and motivation by name but does not elaborate on their acceptable values, and it does not discuss the optional audience or leaders_count parameters. It does instruct to map free-text answers to the closest valid id, which indirectly relates to parameter handling, but this is more of an agent instruction than a parameter explanation. Thus, the description adds minimal semantic value beyond the 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 clearly states the tool resolves role_band + motivation to return suggested focus areas and a recommended package with real prices, using the same routing as the website. This is specific about verb, resource, and expected output, though it does not explicitly differentiate among sibling tools. The mention of composing a mentoring brief as a next step gives workflow context but not a direct comparative contrast.

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 provides a clear next-step ('Next: agree focus areas with the visitor... then compose_mentoring_brief') and instructs to re-ask on bad input rather than guessing. However, it does not state when to use this tool versus alternatives like get_mentoring_options or compose_mentoring_brief, nor does it mention prerequisites such as obtaining the role_band and motivation ids from another tool. The usage context is implied rather than explicit.

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