switch_model

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context beyond basic functionality: it explains that changes are 'persisted to settings.json for consistency across restarts,' which is a key behavioral trait not inferable from the schema. However, it doesn't cover potential side effects, error conditions, or permissions needed, keeping it from a perfect score.

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 well-structured and appropriately sized. It starts with a clear purpose statement, followed by context sentences that earn their place by explaining benefits and persistence. The 'Args' and 'Returns' sections are front-loaded with critical information, and there is no redundant or wasteful text.

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?

Given the tool's complexity (runtime model switching with persistence), no annotations, and an output schema present (which handles return values), the description is mostly complete. It covers purpose, parameters, and key behavioral context. However, it lacks details on error handling or integration with sibling tools, preventing a perfect score.

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 description coverage is 0%, so the description must compensate fully. It does so effectively: the 'Args' section clearly defines both parameters ('tier' and 'model_name'), including allowed values for 'tier' ('quick', 'coder', 'moe', or 'thinking') and constraints for 'model_name' ('must be available in the current backend'). This adds essential meaning beyond the bare schema.

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 tool's purpose: 'Switch the model for a specific tier at runtime.' It specifies the verb ('switch'), resource ('model'), and scope ('for a specific tier'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'switch_backend' or 'get_model_info_tool', which would be needed for a perfect score.

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 some implied usage context: 'This allows dynamic model experimentation without restarting the server' suggests when to use it (for runtime model changes). However, it doesn't explicitly state when to use this tool versus alternatives like 'switch_backend' or 'models', nor does it mention prerequisites or exclusions, leaving gaps in guidance.

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