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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context by specifying the exact metadata contents (fields/schema, themes, record count) and the intended pre-query role, which is not expressed in the annotations.

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?

One sentence carries the core purpose, return contents, and usage timing without redundancy. The most important information is front-loaded, and every clause earns its place.

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 tool with one parameter and strong annotations, the description covers the essential return values and when to invoke it. Even without an output schema, an agent knows what to expect and how to use the result before calling query.

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 single parameter is fully described in the schema as 'Dataset id from search_datasets,' so the description does not need to add much. The description does not introduce additional parameter-level detail beyond what the schema already provides, so baseline 3 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 states a specific verb ('Get metadata'), a specific resource ('one Loire-Atlantique Open Data dataset'), and details the content returned (fields/schema, themes, record count). This clearly differentiates it from sibling tools like search_datasets and query.

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 description explicitly says 'call before query to learn the column names,' which gives a clear usage context and sequencing relative to the query tool. It does not enumerate exclusions or alternative conditions, but the guidance is direct and actionable.

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