ml_model_create

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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the mutating nature is covered. The description adds the long-running behavior, which is a useful disclosure beyond annotations. However, it does not say what the operation returns, how to poll status, or whether creation can be retried on 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?

A single sentence with no filler; the core action and distinguishing context are front-loaded. The long-running note is compact and informative. Nothing could be removed without losing value.

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?

The tool is a simple create with 3 fully documented parameters and no output schema, so the description only needs to cover operational behavior. The long-running warning is included, but the missing details about result retrieval or operation tracking leave an agent uncertain about next steps. Overall adequate but with clear 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 description coverage is 100%, so workspaceId, displayName, and description are all documented in the schema. The description adds no additional parameter-level meaning, so the baseline of 3 applies.

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 begins with a specific verb 'Create' and names the resource 'ML model' with scope 'in a workspace'. This clearly differentiates it from sibling tools like ml_model_update, ml_model_delete, and ml_model_list. The parenthetical 'long-running' adds a defining behavioral trait that aids selection.

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 gives some context (workspace scoping, long-running) but provides no explicit when-to-use guidance or exclusions. It does not mention alternatives, such as ml_model_update for modifying an existing model or ml_experiment_create for experiments. The intended usage is implied by the name rather than stated.

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