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

Annotations already indicate a safe read operation, and the description adds valuable context about the return format (name + French label) and the normalization pitfall (e.g., 'Bern' not 'Berne'). This goes beyond the structured data to help the agent understand expected output and potential traps.

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 three sentences long, front-loaded with the primary action, and every sentence adds essential information. It is concise without unnecessary fluff.

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 simple parameterless tool, the description covers purpose, usage, return values, and pitfalls. It is fully self-contained and even provides cross-references to search_classifieds, making it complete for an agent to use 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?

The tool has zero parameters, so the schema provides complete coverage. The description does not need to explain parameters, and the baseline of 4 is appropriate given the lack of parameters to document.

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 function: listing Swiss cantons supported by the canton filter. It distinguishes this from sibling tools by specifying the resource (cantons) and the context (Joomil's canton filter), making it unambiguous.

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?

Explicitly instructs to call this before filtering by canton and explains why (API values are non-obvious). This gives clear when-to-use guidance without needing to reference alternatives, as the use case is specific and self-contained.

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