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

Annotations already indicate read-only, idempotent, open-world behavior. The description adds that data is Swiss OGD, no auth, commercial use permitted, which is useful context. It also clarifies the output (dataset IDs) and filtering behavior, complementing the annotations without contradiction.

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 concise and front-loaded with purpose, then provides filtering codes and downstream usage. Every sentence earns its place without redundancy, and the key information is presented early.

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 tool with two optional parameters and an existing output schema, the description covers purpose, filtering, and downstream usage. It also includes licensing and auth requirements, making it complete for an agent to decide when and how to call it.

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 description coverage is 100%, so the schema already documents both parameters, including the subject code examples and default limit. The description largely repeats the schema information, adding little beyond it, though it does reinforce the purpose of the subject filter.

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 browses the Swiss FSO STAT-TAB catalog of 648 datasets, and it explicitly mentions filtering by subject area. It also distinguishes itself from siblings by noting it returns dataset IDs needed for table.metadata and table.query, making its role in the workflow 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?

The description indicates this tool provides database IDs for downstream tools (table.metadata, table.query), implying it is for discovery. It does not explicitly state when not to use it or name alternatives, but the mention of the needed IDs gives clear context for when to use it.

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