List Students Needing Attention

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

The annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds behavioral transparency beyond annotations by clarifying that the report is factual, not predictive, and that each finding lists the exact signals and thresholds used. This gives the agent a clear understanding of what the output will contain and how it is generated.

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 two sentences long, front-loads the purpose, and includes all necessary information without fluff. It efficiently explains the tool's function, the nature of the output, and permission requirements. Every sentence earns its place.

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 tool's moderate complexity (5 parameters, no output schema), the description is complete. It explains the overall purpose, the criteria considered, the nature of the output (factual report with signals and thresholds), and permission requirements. Since the schema covers all parameters and the description explains the expected output, the agent has sufficient information to decide when and how to use the tool.

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 100% documentation coverage for all parameters, so the descriptions already explain each parameter's meaning (e.g., min_late, min_missing). The tool description does not add additional parameter-level detail, but it does provide context for how these parameters work together (as thresholds for the signals). Since the schema covers parameter semantics fully, a baseline of 3 is appropriate.

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's purpose: it reports students who may need instructor attention based on specific criteria (inactivity, missing/late submissions, low score). It uses a specific verb ('Report') and clearly delineates the resource ('students needing attention'). This distinguishes it from sibling tools like 'list_submissions_comments_needing_attention' (which focuses on comments) and 'get_missing_submissions' (which only covers missing submissions).

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 clear context on when to use this tool: when you need a comprehensive report of at-risk students based on multiple signals. It mentions required permissions ('Requires instructor/TA permissions'), which gives usage context. However, it does not explicitly name alternatives or state when NOT to use it, so it lacks explicit exclusionary guidance.

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