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

With no annotations, the description must carry the full behavioral load. It usefully discloses the aggregation scope ('across all learners') and the returned measures (error frequencies, candidate counts, mastery recovery rates), but says nothing about read-only safety, whether results are ranked, or how the limit behaves.

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?

A single front-loaded sentence with the resource and scope stated first and the return measures appended. It is dense but every clause carries information; no filler.

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 no output schema and no annotations, the description partially compensates by naming the returned metrics and the segmentation. Remaining gaps are the read-only/ranking behavior and limit semantics, but an agent has enough to select and call it correctly.

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 the enum and limit defaults are already documented. The description only reframes the sections as statutory sections, adding minor domain meaning beyond the schema, which fits the baseline 3.

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?

States a specific verb ('Retrieve') and a specific aggregate resource ('South Africa's most failed K53 test questions across all learners'), with the segmentation dimension named. It does not, however, explicitly distinguish itself from siblings such as get_outcomes_summary or get_platform_stats, so the agent must infer the boundary.

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: 'across all learners' hints that this is the aggregate-analytics option versus candidate-specific tools like get_candidate_dossier. There is no explicit when-to-use, when-not-to-use, or named alternative.

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