ml_model_get

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds no behavioral disclosure beyond the tool name, such as what 'details' are returned, error handling, or whether the model must exist. Minimal additional value beyond the annotations.

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 concise sentence with no filler or redundancy. It front-loads the action and resource. It's efficient, though generic enough that it adds little beyond the tool name.

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 read-only get tool with annotations and a fully documented two-parameter schema, the description is minimally sufficient. However, there is no output schema and the description doesn't state what details are returned or any not-found behavior. An agent could invoke it, but would lack expected response context.

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 100% with descriptions for both mlModelId and workspaceId, so the description doesn't need to re-explain them. It also doesn't add extra meaning about how the IDs relate or which is used for scoping. Baseline 3 is appropriate because the schema carries the parameter semantics.

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 ('Get details') and the resource ('a specific ML model'), which distinguishes it from list-style siblings like ml_model_list. It doesn't explicitly say 'by ID', but the schema provides mlModelId. It's slightly generic but unambiguous enough.

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 phrase 'a specific ML model' implies this is for fetching a single existing model rather than listing them, but the description gives no explicit when-to-use or when-not-to-use guidance. It doesn't mention alternatives like ml_model_list or note any workspace context requirements. Usage is implied rather than stated.

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