ml_model_delete

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the destructive nature is communicated at the structured level. The description does not add extra context about permanence, cascade effects, or required permissions, but it does not contradict the annotations either.

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 entire description is a single front-loaded sentence with no filler or redundant elaboration. Every word contributes to stating the operation.

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?

For a simple two-parameter delete operation with a destructive annotation and full schema coverage, the description is largely sufficient. It does not explain whether deletion is reversible or whether the model must be unused, but the low complexity and annotations cover the main operational risks.

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?

The input schema has 100% description coverage and clearly explains mlModelId and workspaceId, so the description does not need to repeat them. The tool-level description adds no additional meaning about how the parameters relate to the deletion operation, which keeps this at the baseline.

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 states a clear verb ('Delete') and resource ('ML model'), so an agent can tell it apart from ml_model_get, ml_model_update, and unrelated delete tools. It lacks scoping details like workspace-level specificity or a note that this differs from deleting an experiment, but the core purpose is unambiguous.

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?

There is no guidance on when to use this tool instead of alternatives such as ml_model_update or ml_model_delete-related operations, nor any mention of prerequisites like listing models to obtain mlModelId. The agent must infer usage entirely from the tool name and schema.

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