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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds the 'access, freshness, and coverage details' context which enriches what the agent knows about the returned content. However, it doesn't describe pagination, limits, or return format beyond what the annotations/schema already implicitly convey. With good annotations, a 3 is appropriate.

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?

Single sentence, zero waste. Every clause earns its place: the verb 'list', the resource 'datasets', the content dimensions (access, freshness, coverage), and the purpose (selection). Very efficiently packed.

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?

This is a simple parameterless list tool with strong annotations (readOnly, idempotent, non-destructive) and an output schema. The description adequately conveys the three data dimensions returned (access, freshness, coverage). It's complete for a simple enumeration tool. Could potentially mention where this fits in the workflow (e.g., before datasets_describe), but for a list tool this is sufficient.

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?

This tool has 0 parameters and schema coverage is 100% (trivially, since there are no params to document). Per the rubric, 0 params receives a baseline of 4. The description appropriately focuses on output characteristics rather than parameter semantics, which is correct for a parameterless list endpoint.

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 states a clear purpose: list available datasets with access, freshness, and coverage details, specifically for dataset selection. It uses a specific verb (list) plus resource (datasets) and adds the contextual purpose of selection. It doesn't explicitly distinguish from siblings like datasets_describe or datasets_search, but the 'list' vs 'describe'/'search' distinction is fairly clear from the verb use.

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 usage context ('for dataset selection') which gives a hint about when to use it. However, it doesn't explicitly say when NOT to use it or point to alternatives like datasets_describe for detailed info on a single dataset or datasets_search for filtered lookups. The usage guidance is implied rather than explicit.

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