list_curated

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

With no annotations, the description carries the full burden. It reveals that the tool returns a sorted list of 7 entries and provides examples. It does not discuss error handling or immutability, but for a simple read-only list, this is adequate.

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, front-loading the main purpose, and provides a clear example and summary. Every sentence adds value without redundancy.

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, the description is complete. It explains what the tool does, what it returns, and its typical use. The output schema is present, but the description already covers the return structure.

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 input schema has no parameters, so the description adds value by explaining the output and purpose. Baseline for 0 parameters is 4, and the description appropriately compensates for the lack of parameter details.

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 that this tool lists the 7 curated AEMO NEM dataset IDs, which covers ~95% of typical queries. It distinguishes itself from sibling tools like 'get_data' and 'search_datasets' by being a no-parameter listing operation.

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 explains that the tool returns the most common datasets, implying it should be used first for typical analytical queries. However, it does not explicitly state when not to use it or compare with alternatives like 'search_datasets'.

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