list_missing_submissions

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

With no annotations, the description carries the full burden. It clearly defines 'missing' as past-due and not submitted, and states the output is grouped by assignment, effectively conveying the operation's behavior. It implies a read-only list operation, though it does not explicitly state read-only status or mention pagination/size concerns. The description adds meaningful behavioral context beyond the schema.

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 sentence definition: the first line states the core action and scope, the second provides a concrete use case. No redundant words, front-loaded with the key purpose. Excellent efficiency and structure.

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 a single parameter, a clear purpose, and an output schema (indicated by 'Has output schema: true'), the description covers the essential aspects: it explains what is returned (students grouped by assignment) and the criterion for missing. It does not mention edge cases like assignments with no missing students or whether it includes all course assignments, but these are minor given the output schema and the tool's focused nature.

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 one parameter, course_identifier, with 0% schema description coverage, so the description must compensate. The description fails to mention the parameter at all, offering no guidance on value format (e.g., ID vs. name) or any constraints. The parameter name is somewhat self-explanatory, but no additional semantics are provided, leaving the agent to guess.

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 states a specific verb ('List'), a precise resource ('students with missing work'), scope ('across all assignments in a course'), and a grouping ('by assignment'). It also includes a concrete query example ('who hasn't turned in X?') that clearly differentiates it from sibling tools like list_submissions (which lists all submissions) and list_ungraded_submissions (which filters on grading status, not missing).

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?

Provides a direct usage scenario ('Use for who hasn't turned in X?'), giving clear contextual guidance. However, it does not explicitly mention when NOT to use it or name alternative tools for overlapping cases (e.g., list_submissions for complete submission data), leaving some inference to the agent. Note: the sibling list is extensive, but the description offers no exclusions.

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