list_curated

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

With no annotations provided, the description carries the full burden of behavioral transparency. It discloses that the tool returns a sorted list of dataset IDs and that each ID is documented via describe_dataset. However, it does not mention any potential side effects, authentication needs, or performance characteristics, but for a read-only listing operation, 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, consisting of four sentences. It front-loads the main purpose, then provides supplementary context, and ends with the return format. 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 simple nature of the tool (no parameters, straightforward output), the description is complete. It explains the purpose, the meaning of 'curated', the documentation reference, and the return format (sorted list). The presence of an output schema fills in any additional details about 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 tool has no parameters, so the schema provides no details. The description adds meaning by explaining what 'curated' means (datasets that accept plain-English filter keys in get_data and return aliased, well-typed measure columns). This adds semantic value beyond the empty 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 clearly states the tool's purpose: 'List every curated dataset ID in this version of ato-mcp.' It specifies the verb 'list', the resource 'curated dataset IDs', and the scope 'in this version'. Additionally, it distinguishes from sibling tools like search_datasets by explaining that these are the datasets where get_data accepts plain-English filter keys.

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?

While the description does not explicitly say when not to use this tool, it provides implicit guidance by explaining that this tool lists datasets that support plain-English filter keys in get_data. This helps an agent decide when to use this tool versus search_datasets or other listing tools.

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