data_table_create

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 reveals the confirmation requirement ('Requires confirm=True after user approval'), which is valuable behavioral context. However, it doesn't disclose other important traits like whether this is a destructive operation, what permissions are needed, rate limits, or what happens on success/failure.

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 appropriately sized with a clear two-part structure: purpose statement followed by parameter explanations. Every sentence earns its place by providing essential information. However, the parameter explanations could be more efficiently integrated into the main description rather than as a separate 'Args:' section.

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 5 parameters with 0% schema coverage and no annotations, but with an output schema present, the description is moderately complete. It covers the confirmation requirement and some parameter semantics, but doesn't provide enough context about the mutation nature of this tool, error conditions, or relationship to other notebook operations. The output schema reduces the need to describe return values, but more behavioral context would be helpful.

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?

With 0% schema description coverage, the description must compensate but only partially succeeds. It provides some semantic context for parameters (e.g., 'description of the data table to create', 'Must be True after user approval' for confirm), but doesn't explain 'notebook_id' (UUID format), 'source_ids' (what sources are), or 'language' (what codes are valid). The description adds value but doesn't fully compensate for the schema coverage gap.

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 the verb 'Generate' and resource 'data table', making the purpose explicit. It distinguishes from siblings like 'notebook_create' or 'report_create' by focusing specifically on data table generation. However, it doesn't explicitly differentiate from similar tools like 'quiz_create' or 'slide_deck_create' in terms of output type.

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 provides clear context about when to use this tool: 'after user approval' with 'confirm=True'. This gives important guidance about the confirmation requirement. However, it doesn't specify when NOT to use this tool or mention alternatives among the sibling tools.

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