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

Annotations already convey read-only, idempotent, non-destructive behavior, lowering the description's burden. The description adds useful context about what metrics are returned, but it does not disclose behavioral details such as aggregation timing, data freshness, or response shape. This is adequate but not rich.

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 with no filler. The first sentence compactly enumerates the counted categories, and the second adds valuable context about the dashboard provenance without redundant wording.

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 zero-parameter, read-only metric tool with an output schema available, the description gives a complete mental model: what is counted, across what scope, and where the numbers come from. Nothing needed to invoke it correctly is missing.

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 tool has zero parameters and the input schema fully covers that surface (100% coverage). With no parameters to explain, the description does not need to add parameter-level semantics; the baseline of 4 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 opens with a specific verb ('Counts') and identifies the resource and metric categories: courses by status, learners, runs in progress/completed, and certificates issued. It clearly describes what the tool does, though it does not explicitly differentiate itself from similar siblings like get_usage or get_workspace_info.

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 phrase 'the numbers behind the dashboard's overview page' provides clear context for when this tool is relevant. It implies the tool is for overall workspace counts/statistics, but it does not mention when not to use it or name alternatives.

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