List AI datasets

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds nothing behavioral beyond duplicating '[READ]' and an opaque path mapping; no return shape, pagination, or scope information is given.

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?

It is short, so it wastes no space, but the single sentence is cryptic pseudo-structure (arrow notation, base-path jargon) rather than front-loaded meaning. Brevity here reflects under-specification, not efficiency.

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?

With no output schema and no annotations explaining scope, the description should at least say what datasets are and roughly what the response contains. Instead it offers an internal path mapping, leaving an agent unable to describe the return or distinguish this from list_ai_buckets.

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 takes zero parameters, so the schema burden is nil and the baseline is 4. The description does not need to explain parameter semantics, and its route-mapping sentence adds no parameter confusion.

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 name and title state a clear verb+resource (list AI datasets), but the description itself only maps the tool to an OpenAPI route and never says what a 'dataset' is or what listing returns. The phrase 'buckets path under /ai/v1 base as datasets' is confusing rather than clarifying, and gives no differentiation from sibling list_ai_buckets.

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 only guidance is the '[READ]' tag, which restates the readOnlyHint annotation. There is no statement of when to call this versus list_ai_buckets, list_ai_models, or list_ai_knowledge_bases, nor any prerequisite or filtering context.

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