model_switch

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

The description carries the full burden since no annotations exist. It usefully discloses the grayscale vs full promotion behavior and the mandatory human review at 100, but it does not mention state-change risk, reversibility, the human_confirmed hang condition, or event-trail behavior—all significant for a production model switch.

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?

One compact sentence with the core semantics front-loaded; every phrase carries meaning and nothing is wasted. It is well-sized for the task.

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 7-parameter mutating tool with no annotations and no output schema, the description plus rich schema covers parameter meaning but leaves out the switch-flow lifecycle (e.g., pending human review, rollback semantics, event logging implied by comment). It is adequate but has 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 the schema already documents all 7 parameters. The description adds a little context by linking percent to grayscale vs full human review, but no supplementary meaning beyond the schema; baseline 3 is appropriate.

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 identifies a specific operation—switching the active model by lane/gear—and states the core percent semantics (grayscale below 100, full promotion with mandatory human review at 100). It does not explicitly contrast with sibling tools like model_register or model_unregister, so it stops short of full sibling differentiation.

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 explicit when-to-use guidance or alternatives are given; the agent must infer from the action enum. There is no exclusion such as 'for model registration use model_register', so guidance is effectively absent.

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