update_ml_model

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

Without annotations, the description must disclose behavioral traits, but it only says 'update an existing ML model'. It does not mention whether the update is partial or full, idempotency, error handling, or required permissions.

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?

Single sentence, no fluff, and clearly front-loads the purpose. However, it is too concise for a tool with complex parameters, sacrificing value for brevity.

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 complexity of updating an ML model with an object parameter and no output schema or annotations, the description is incomplete. It fails to explain return values, request format, or behavioral details.

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?

Both parameters (model_id, model_data) have zero description in the schema, and the tool description adds no meaning. 'model_data' is an object but its structure is completely unspecified, leaving the agent to guess.

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 action 'update' and the resource 'ML model in OpenMetadata', making it distinct from sibling tools like create_ml_model or delete_ml_model.

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 vs alternatives, no prerequisites, or when not to use. The description does not help the agent decide between this and other update tools.

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