get_dimension_items

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

No annotations are provided, so the description must carry the full burden. It explains pagination (offset, limit with defaults) and mentions dependencies on other tools, but omits behavioral details like side effects (none expected), authorization needs, or data freshness. It does not contradict any annotation since there are none.

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: one opening sentence followed by a structured list of parameters. Every sentence adds value and there is no redundancy. It is appropriately front-loaded with the purpose.

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?

The tool has 4 parameters (one required) and an output schema, which covers the return format. The description explains parameter usage and integration with sibling tools. It lacks mention of error handling or edge cases, but the output schema likely documents the response structure. Overall adequate for a paginated listing 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?

The schema has 0% description coverage, so the description fully compensates. It explains dimension_code as coming from get_dataset() output, level as a hierarchy filter with examples, offset as skip, and limit as max items with default and maximum. This adds significant meaning beyond the schema's type and title fields.

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 verb 'Get' and resource 'possible values for a dimension' with concrete examples (years, regions, categories). It distinguishes from siblings by explaining how dimension codes from get_dataset() are used and that item codes feed into get_value() and custom_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 implies when to use: after get_dataset() to obtain dimension codes, and before get_value() or custom_query() which need item codes. It does not explicitly state when not to use, but the context is clear enough for an AI agent to infer appropriate usage.

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