list_datasets

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 disclosure. It clearly discloses pagination behavior, instructs the agent to use next_offset rather than raising the limit, and specifies the exact response shape. This goes well beyond a simple 'lists datasets' and gives the agent actionable behavior expectations.

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 well-structured and front-loaded: a one-sentence purpose, a usage context line, then a compact Args/Returns/Example layout. Every section earns its place and the example is genuinely useful. No filler or redundant restatement.

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?

For a two-parameter list tool, this description is complete: it covers behavior, pagination, parameter semantics, and return shape, and even gives an example. The presence of an output schema means the described Returns section is a bonus, and the pagination warning covers a common mistake. Nothing essential is missing.

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?

Schema description coverage is 0%, so the description must fully compensate. It does: the Args section adds meaning beyond the schema by explaining limit as page size with a valid range (1-100), explaining offset as the number of datasets to skip, and restating defaults. This is exactly what an agent needs to call the tool correctly.

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 opens with a specific verb-resource pair: 'List all datasets' and adds the distinguishing detail of row counts and a name:type column summary. This sets it apart from siblings like search_rows, create_dataset, and describe_dataset, so an agent can tell what it does without inspecting the schema.

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 explicitly states when to use the tool: 'Use this to discover what the user is already tracking before creating or querying a dataset.' This is clear contextual guidance, though it does not explicitly name alternative tools or exclusion conditions, so it falls just short of a 5.

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