set_model

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

With no annotations, the description must disclose all behavioral traits. It mentions hot-reloading the Python engine if active, which is good. However, it does not explain behavior when the engine is not active (e.g., does it queue the change?), nor potential side effects like interrupting current transcription. This leaves some ambiguity.

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, each serving a distinct purpose: stating the primary action and the method, and adding a usage constraint. No redundant words.

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, single-parameter tool with no output schema, the description covers the essential aspects: what it does, how it works, and a prerequisite. It could be improved by clarifying behavior in edge cases (e.g., no active engine), but is otherwise complete enough for an AI agent.

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 schema provides 100% coverage with a description for the only parameter. The tool description adds value by stating the constraint that the model must be in the WindyTune ladder, which is not present in the schema. This helps the agent select a valid value.

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 uses a specific verb-resource pair ('Switch the active transcription model') and clearly distinguishes from related tools like list_models. It also adds implementation details (hot-reload via WebSocket) that make the tool's purpose unmistakable.

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 a clear prerequisite: the model must be in the WindyTune ladder, and instructs users to consult list_models. It does not explicitly state when not to use this tool, but the guidance is sufficient for typical scenarios.

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