list_data

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

Beyond the annotations, the description discloses the organization scope ('lists only the active API key's organization'), that it is not separately rate-limited, and the full return shape including the datasets array fields and pagination metadata. This is substantial addition beyond the readOnlyHint and idempotentHint annotations, giving the agent confidence about side effects and output.

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 dense and packs a lot of actionable information into a compact paragraph. It front-loads the purpose, then usage, then return shape, then comparison. It's slightly long but every sentence carries value, with no filler or repetition.

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 read-only list tool with no output schema, the description covers purpose, usage workflow, return fields, and differentiation from the sibling list_datasets. It also addresses operational details (organization scope, rate limiting) that an agent needs to call it appropriately. Nothing critical 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?

The input schema already documents all six parameters with descriptions, covering 100% of the schema. The description adds some contextual guidance (e.g., pairing search with compact mode) but does not explain individual parameter values beyond what schema provides. This meets the baseline for high schema coverage.

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 visible datasets for the current user.' It also explicitly differentiates from the sibling list_datasets, which handles a different store ('semantic-training store with model_tier and training status'), and routes the agent to get_data_schema for schema retrieval. This makes the tool's purpose unambiguous.

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 explicit workflow guidance: 'Use search plus compact mode first for low-token dataset discovery, then get_data_schema on the chosen dataset_id.' It also names the alternative list_datasets and the exact conditions under which to choose each ('browse datasets from the connect_data and register_source flows... use list_datasets for the semantic-training store'). This is active routing, not just implied context.

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