start_generation_workflow

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

No annotations, so the description carries the full burden. It doesn't disclose that this is an asynchronous workflow start, what it returns (a workflow ID, a job handle?), how to poll progress (get_workflow_logs?), or any cost/rate implications — all critical for a workflow-launcher.

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?

One sentence, front-loaded with the action and purpose. No filler; every word earns its place.

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?

A workflow-launching mutation with no annotations and no output schema leaves the agent without the async/return/polling context it needs. The existence of get_workflow_logs and obtain_synthetic_dataset_id siblings hints at the lifecycle, but the description doesn't connect them.

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% and the single required model_id is documented with a pointer to get_trained_model_id. The description adds only the 'trained TabGAN model' qualifier, so it earns the baseline rather than more.

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?

States a specific verb+resource+outcome: 'Start a workflow to generate synthetic data from a trained TabGAN model.' The 'generation' framing distinguishes it from the sibling start_training_workflow, though it doesn't name that sibling explicitly.

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 when-to-use or when-not-to-use guidance. It doesn't state prerequisites (e.g., that a model must already be trained and its ID obtained via get_trained_model_id) or how it relates to obtain_synthetic_dataset_id, which likely consumes this workflow's output.

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