get-ml-model

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

No annotations are provided, so the description carries the full burden. It only states 'Get ML model details' but does not disclose any behavioral traits such as error handling, response structure, or side effects. This is insufficient 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 front-loads the verb and resource. It is appropriately sized with no wasted words, though it could benefit from slightly more detail.

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 tool has 4 parameters (1 required) and no output schema, the description is too sparse. It does not explain what 'details' are returned, how optional parameters like 'fields' or 'include' affect the response, or any expected behavior. This leaves the agent underinformed.

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 coverage is 75%, so the baseline is 3. The description does not add extra meaning to the parameters beyond what the schema provides. The 'include' parameter has an enum but no description; the description does not clarify it. Overall, the description adds minimal parameter context.

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 verb 'Get' and the resource 'ML model details' with a specific identifier 'UUID'. This distinguishes it from sibling tools like 'get-ml-model-by-name' which uses a different lookup method.

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 provides no guidance on when to use this tool versus alternatives such as 'get-ml-model-by-name'. Without any exclusion criteria or context, the agent has no basis to choose between them.

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