announce_model

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

The description discloses multiple behavioral traits beyond the annotations: idempotency ('a second call with the same model is a cheap no-op'), the fuzzy tier resolution order, and the side effect of narrowing the tool list. These details help the agent anticipate what will happen when it calls the tool, well beyond the sparse 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 two sentences, front-loaded with the action and purpose, then detailed resolution logic, and ends with usage guidance. Every clause carries value, and there is no fluff.

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 single-parameter tool with no output schema, the description covers purpose, effect, idempotency, and when to use it. Nothing an agent needs to decide whether to call it or to understand the outcome is missing.

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?

The input schema already provides a clear description of the model parameter with an example ('claude-haiku-4-5'), so schema coverage is 100%. The description adds little beyond restating that the agent reports its model identifier, which is already implied by 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 states a specific verb ('self-reports') and resource ('active model identifier'), and clearly explains the consequence: the server resolves to a tier and narrows the exposed tool list. It also distinguishes itself from plan_turn by name, so an agent can tell them apart without opening the schema.

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?

Explicit guidance is given: 'Prefer calling plan_turn(model=...) for routine per-task use; use announce_model as a fallback when plan_turn is not appropriate.' This tells the agent exactly when to use this tool and when to use the alternative, leaving nothing to inference.

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