Set Model Tiers

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

The description explicitly states it overwrites previously saved tiers entirely, adding behavioral context beyond annotations. Annotations already indicate idempotence, but the description clarifies the overwriting behavior. No contradiction with 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?

Three efficient sentences: first states purpose, second explains process, third details effect. No wasted words, and critical information is front-loaded.

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?

The tool is simple with one parameter, full schema coverage, and an output schema. The description covers the key behavior (overwrite) and is complete for this complexity level, though it omits error handling details.

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 coverage is 100% and the schema already describes the tiers parameter as a mapping to allowed tier values. The description adds no further meaning, so baseline score 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 clearly states the tool saves model tier classifications, using a specific verb (Save) and resource (model tier classifications). It distinguishes from sibling tools like prompt and set_preferences, which deal with prompts and preferences.

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 guidance on when to use vs. alternatives. The context implies it is for persisting model tiers after sampling/benchmarks, but lacks explicit when-not-to-use or comparison with siblings.

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