Find a French department or region

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

Annotations already cover read-only and idempotent hints. The description adds valuable context: it's accent-insensitive, uses pre-2016 regions, and makes no network request. This goes beyond annotations and explains behavior that could affect results.

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 sentences, front-loaded with purpose and examples. The second sentence adds crucial behavioral notes without fluff. Nothing extraneous.

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 the tool's simplicity (2 params, no output schema), the description is complete: it states what it returns (department numbers and region ids) and important caveats (pre-2016 regions). It doesn't detail the response structure, but that's not necessary for a resolver tool.

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 covers 'query' with a description, but 'limit' lacks it. The description enriches 'query' with accepted formats (Paris, 75, Rhône) and clarifies matching rules. It doesn't mention 'limit', but the schema's min/max are sufficient. Overall, it adds meaning beyond the schema.

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 resolves free text (city, department, region) to specific department numbers and region ids used by Leboncoin filters. It gives concrete examples and differentiates itself from sibling tools like find_category by focusing on location resolution.

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?

It explains the tool is for converting free text into filter parameters, implying when to use it (before searches). It also notes it's local, but does not explicitly mention alternatives or exclusions. Still, the context is clear enough for a lookup tool.

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