list_curated

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

With no annotations provided, the description must disclose behavior. It states the output is a sorted list of exactly 10 dataflow IDs, and provides the specific IDs. It also notes this is consistent ('Always 10 entries today'). This is sufficient transparency for a read-only list operation.

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 well-structured: a one-sentence purpose, bulleted list of dataflows, example, usage scenarios, and return description. Every sentence adds value, and the most critical information is front-loaded.

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 simplicity of the tool (list with no parameters) and no annotations, the description fully covers what an agent needs: purpose, which dataflows, example output, and when to use. No gaps were identified.

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 tool has zero parameters, and schema coverage is 100% (empty schema). The description does not need to add parameter details. It provides relevant context about the fixed list without wasting space. This exceeds the baseline of 3 for high schema coverage.

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 explicitly states the tool lists the 10 curated ABS dataflow IDs with plain-English support. It distinguishes itself from siblings like get_data (raw SDMX) and describe_dataset (metadata). Specific verb 'list' and resource 'curated dataflows' clearly define purpose.

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 includes a 'When to use' section with three clear scenarios (e.g., knowing which dataflows have plain-English support, enumerating capabilities). While it does not explicitly state when not to use, the context of sibling tools and the description implying other dataflows use get_data with raw codes provides implicit guidance.

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