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

Annotations already establish that this is a safe, read-only, idempotent operation, so the bar is lower. The description still adds meaningful behavioral context: it reports advertised hourly rates via median and interquartile range, includes sample sizes by rates and employers, and suppresses figures when fewer than three employers advertise.

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 front-loaded with the core output and stays compact while covering the key metrics, suppression rule, and optional ordering behavior. Every sentence serves a purpose.

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 annotations covering safety and no output schema, the description provides enough context to invoke the tool correctly: it explains the statistics returned, the minimum-employer threshold, and how the optional kind parameter affects ordering.

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 schema has full coverage for the single enum parameter and already explains that it selects which kind of work appears first. The description reinforces that the named kind is shown first, but adds little semantic detail beyond the schema's own description and example.

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 resource (robot training pay per hour by kind of work) and the specific outputs (median, 25th–75th percentile range, sample counts). It does not explicitly differentiate itself from siblings like get_robot_training_market or search_robot_training_jobs, so it stops short of a 5.

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?

Usage is implied: the tool returns pay figures and the optional kind parameter moves a chosen category to the front. There is no explicit guidance on when to use this instead of the sibling job-search or market tools, nor any stated exclusions.

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