create_dataset

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

With no annotations, the description fully discloses behavior: it creates a new dataset, enforces case-insensitive uniqueness, defines column validation rules, and specifies both success and failure return formats. The example illustrates exact input/output structures.

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?

Organized with purpose, args, returns, and example. Every sentence adds value, and the example is illustrative. Length is justified by tool complexity; nothing is redundant.

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?

Covers what the tool does, when to use it, required pre-call confirmation, parameter semantics, return values, and a realistic example. With no output schema, it still fully explains output structure and error handling.

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 0% (schema only lists names/types), but the description explains each parameter in depth, including column type list, per-type attributes, and constraints (e.g., enum requires at least one option). This vastly exceeds schema detail.

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 explicitly states the tool creates a new dataset (table) with typed columns and gives concrete use cases (candidates, inventory, payments). It clearly distinguishes from siblings like add_column, list_datasets, and delete_dataset.

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?

Provides direct guidance: 'Use this whenever the user wants to start tracking something new' and mandates a workflow ('Always propose the column list... get confirmation before calling'). This is unambiguous and actionable.

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