get_topic_distribution

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

With no annotations, the description carries the behavioral burden and does disclose that this is a read-only ranking of question counts per topic, with optional q_type and exam_code values. It does not explicitly state null-filter behavior, but the schema defaults and optionality make this inferable.

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 no filler. The core behavior and filter values are communicated in a compact and scannable way.

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?

For a low-complexity read-only tool with two optional parameters and an output schema, the description is nearly sufficient. The only missing detail is explicit handling of omitted parameters, which is adequately implied by schema defaults.

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; it does by specifying allowed values for q_type ('mcq'/'essay') and exam_code ('sl1'/'sl2'), which are absent from the schema. It does not fully explain null/default semantics, but the schema already indicates defaults.

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 aggregation task: a topic-heat ranking counting questions per (subsubject, topic). It also names the relevant dimensions and filter values, making the tool's scope distinct from siblings like get_issue_distribution.

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?

There is no guidance about when to use this tool versus alternative siblings such as get_issue_distribution or get_weak_topics. The filters are described, but no context is given for choosing this tool in a workflow.

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