create-ml-model

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

With no annotations, the description must convey behavioral traits, but it only states the action. Missing critical details: idempotency, overwrite behavior, required permissions, side effects like duplicate name handling, or return value. This is inadequate for safe invocation.

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 a single sentence that immediately states the purpose. It is concise with no wasted words, though it could include additional context without becoming verbose.

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 9 parameters (3 required) and no output schema, the description is extremely incomplete. It fails to explain post-creation behavior, default values, or how this tool fits into the broader workflow. Completely lacking needed context for an AI agent.

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 each parameter has a basic description. The tool description adds no additional meaning beyond the schema, meeting the baseline. No further enrichment is provided.

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 'Create a new ML model' clearly states the action (create) and resource (ML model). It distinguishes from sibling create tools (e.g., create-chart, create-classification) by specifying the resource type, and from update-ml-model by indicating creation rather than update.

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 is provided on when to use this tool versus alternatives (e.g., update-ml-model for modifications). The description does not mention prerequisites, limitations, or contextual scenarios.

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