simulate_student_review

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

No annotations are provided, so the description carries the full burden, and it does substantial work by disclosing what the tool returns, including the veto warning and plagiarism pre-check, which are non-obvious behavioral traits. It does not say whether the simulation is read-only or stateful, how long it takes, or which skill_name values are valid.

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?

Two purpose sentences followed by a compact parameter list; the output enumeration is front-loaded and every clause conveys information an agent needs. No filler or repetition of structured fields beyond the useful glosses on the ambiguous parameters.

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 not be spelled out, yet the description leaves open how text and data interact (precedence, whether both may be passed) and where skill_name values come from (likely list_student_skills). For a tool where only skill_name is required, an agent could invoke it with no reviewable content and get nothing useful.

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 must compensate, and it defines all three parameters: skill_name is the track name with concrete examples ('innovation_research', 'challenge_cup'), text is the application body, and data is the structured application dictionary. It omits the critical constraint that at least one of text/data must be supplied, given only skill_name is required by 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 first sentence names a specific verb and resource (execute a review simulation and score a student's application text or data), and the second enumerates the concrete outputs (dimension scores, overall grade, judge comments, veto warning, plagiarism pre-check, improvement suggestions). None of the siblings (list_*, get_*, route_*, generate_student_doc) overlap, so an agent can distinguish it, though the description never explicitly contrasts itself with an alternative.

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 only implied by the subject matter: you call it when you want a simulated review of an application. There is no statement of when to prefer it over generate_student_doc, no prerequisites, and no exclusion criteria, so the agent must infer the trigger conditions.

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