model_register

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

Annotations are absent, so the description carries the full disclosure burden. It only says 'register' and does not mention persistence, duplicate-name behavior, hash verification, validation failure, or reversibility. For a mutating registry tool this is a meaningful gap.

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 front-loaded sentence with no wasted words, and the parenthetical 'name+endpoint+model' is useful shorthand. The leading '模型注册' repeats the tool name, but it does not materially harm clarity.

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?

This is a complex tool with 10 parameters, a nested profile object, conditional requirements, and no annotations or output schema. The one-line description does not cover side effects, success/failure behavior, duplicate-name outcomes, or verify_hash implications, leaving an agent under-informed for a mutating registration operation.

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%, and each parameter already has detailed descriptions, so the baseline is 3. The description adds no real semantic value beyond the schema; its mention of name+endpoint+model is redundant shorthand.

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 specific action ('注册' / register) and resource ('trained model endpoint'), and even summarizes the core fields as name+endpoint+model. It is distinguishable from model_unregister and model_switch by its verb, though it does not explicitly name those siblings.

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 'trained model' implies the tool should be used after training, but there is no explicit guidance about when to use it vs alternatives like model_switch or model_unregister. No when-not-to-use conditions or lifecycle context are provided.

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