llm_classify

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

The description fully discloses the decision algorithm for model selection based on complexity and budget, including specific thresholds. However, it does not mention any side effects or permissions, which would be expected given no 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 well-structured with bullet points, front-loaded purpose, and no redundant sentences. Every sentence adds value.

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, the description provides sufficient context for correct invocation, covering logic, parameters, and constraints. The presence of an output schema means return values are documented elsewhere.

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?

With 0% schema coverage, the description adds significant value by explaining each parameter and providing example values for quality and min_model, though exact allowed values are not formally defined.

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 the tool classifies a prompt's complexity and recommends a model, which is specific and distinct from sibling tools like llm_analyze or llm_route.

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 explains when to use the tool and provides detailed logic for complexity and budget-based model selection, but does not explicitly mention when not to use it or compare to alternatives.

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