list_datasets

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful context beyond annotations: the operation is scoped to the active API key's organization, is not separately rate-limited, and returns a specific dataset array shape. No contradiction with annotations.

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 three sentences long, front-loaded with the core purpose, followed by usage guidance and behavioral details. Every sentence adds necessary information: what it lists, how to use it efficiently, and what scope/return fields to expect.

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 present, the description compensates by enumerating the return fields and pagination metadata. It also covers scope, rate limiting, safety profile, and usage flow. For a low-complexity discovery tool with six optional parameters and rich annotations, 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 coverage is 100% with descriptions for all six parameters, so the baseline is 3. The description adds contextual value by linking search and compact mode to low-token discoveryched, but it does not add detail beyond the schema for individual parameters.

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 specific verb and resource: 'List registered datasets and their current model tier.' This clearly separates the tool from data-row operations like list_data and from summary tools. However, it does not explicitly name a sibling alternative, so it stops short of a 5.

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 gives clear context: use search plus compact mode for low-token discovery, then switch to polling status or primary data tools after choosing a dataset. It indicates when to use the tool and provides a discovery-to-data flow, though it refers to 'primary data tools' without naming specific alternatives.

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