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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description nevertheless adds important provenance context: the practice questions are aitrainer.work's own and not a platform's real interview questions, which matters for how an agent presents the output. It says little about how the two optional inputs combine or what happens when one is omitted.

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?

Three sentences with no filler, and the caveat about question provenance is placed last where it reads as a qualifier. The opening leads with a content inventory rather than the action, which is slightly less front-loaded than ideal, but overall it is tightly written.

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?

An output schema exists, so return values need no explanation, and annotations carry the safety profile. With zero required parameters the remaining gap is only the behavior when role and/or platform are omitted, which is minor for a read-only lookup.

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 100%, so both parameters are already fully documented with examples. The description only loosely implies the role/platform scoping ('for a role', 'how a platform hires') without clarifying whether either is optional, what the default is, or whether combining them narrows or broadens results.

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 names the concrete content returned: hiring steps, interview format, aitrainer.work guides, and practice questions with strong-answer outlines for a role. That is clearly distinct from siblings like get_job, get_pay_stats, and search_jobs. It stops short of an explicit 'this is not X' routing statement, but the resource is unambiguous.

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?

It gives two concrete usage contexts: 'explain what an interview involves' and 'run a mock interview.' That is enough for an agent to decide to call it, though no exclusion or alternative tool is named for cases where the agent actually wants the job posting (get_job) instead.

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