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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states the creation action and dataset purpose, but does not mention idempotency, error behavior, required permissions, or whether the operation is synchronous. This lacks critical transparency for an agent.

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 only three sentences, front-loaded with the core action, and efficiently explains the dataset concept and its use with submit_query. No redundant or verbose content.

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?

Given the tool has 5 parameters and no annotations, the description is adequate but lacks details on return values (though output schema exists), default behavior of optional parameters, and any constraints (e.g., name uniqueness). It covers only the minimum needed to understand the tool's main purpose.

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 100%, so baseline is 3. The description does not elaborate on parameter meanings beyond what the schema already provides. For example, 'entity_ids' could use more context on how seeding works, but it is not provided.

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 clearly states 'Create a new dataset' and explains what datasets are (collections of entities). It distinguishes from the sibling tool 'create_dataset_from_csv' by focusing on creating an empty dataset. The connection to submit_query adds specificity.

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 for creating a dataset to be used with queries, but does not explicitly mention when to use this tool versus alternatives like 'create_dataset_from_csv' or 'add_dataset_entities'. No exclusions or prerequisites are stated.

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