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

Annotations already establish read-only, idempotent, non-destructive behavior, so the description needs only to add context; it provides the data source, notes no auth is required, and specifies bilingual output semantics. It does not discuss edge cases like unknown codes, but this is a simple lookup with an output schema.

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 compact and front-loaded: the primary action and example come first, followed by parameter sourcing and access requirements. Every sentence carries useful information with no repetition.

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 two-parameter lookup with a full output schema and safety annotations, the description is complete: it covers purpose, example, where parameters come from, data source, and authentication. Nothing an agent needs 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?

Schema description coverage is 100%, so both parameters are already documented with format and examples. The description reinforces the dimension_code format and its source, but adds no 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 states a specific verb ('Decode'), the resource ('categorical column's codes'), and the exact result (German + English display names) with a concrete example. It also distinguishes itself from the sibling metadata/search tools by referencing dataset_metadata's category_dimensions list as the source of dimension_code.

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 clearly indicates the tool is used after dataset_metadata has supplied the category_dimensions list, and the schema further excludes F- numeric columns. It does not explicitly name sibling alternatives or state 'use this instead of X', but the intended workflow is unambiguous.

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