Generate a data table

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

Beyond the annotation (readOnlyHint: false indicating mutation), the description adds no behavioral context. It does not disclose what happens to the generated table (e.g., stored in notebook, returned inline), permissions needed, or side effects on the notebook.

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 very concise (one sentence), but this brevity comes at the cost of missing important details (e.g., instructions format, output behavior). It is not optimally sized for the tool's complexity.

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 4 parameters and no output schema, the description is incomplete. It fails to explain how to construct the instructions parameter, whether the table is returned or saved, or what the return value looks like. Annotations do not compensate for these gaps.

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 75% (3 of 4 parameters described). The description adds meaningful info for the 'instructions' parameter ('describes the desired columns'), which the schema left undocumented. For other parameters, it adds nothing beyond the schema. Baseline 3 is justified.

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 tool generates a structured data table, with notable verb 'Generate' and specific resource 'data table'. It distinguishes itself from sibling tools like generate_audio or generate_flashcards by specifying a unique 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?

No guidance on when to use this tool versus alternatives. The description does not mention prerequisites, limitations, or contrast with other generate_* tools, leaving the agent without decision support.

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