get_model_recommendation

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

Without annotations, the description must disclose behavior. It only hints at a mapping (analysis vs execution) but omits details like output format, side effects, or whether the recommendation is static. An agent cannot infer response structure or caching behavior.

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 a single sentence with no wasted words. However, it omits critical details that would make it maximally useful, sacrificing completeness for brevity.

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 low schema coverage, no output schema, and no annotations, the description is insufficient. It fails to explain return values, the exact model for each stage, or edge cases. An agent would need to infer or test to use it reliably.

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 0%, so the description should explain parameter semantics. It mentions 'Sonnet for analysis, Haiku for execution' but does not map individual stage enum values to analysis or execution, leaving ambiguity (e.g., is 'test-case-generation' analysis or execution?). The description adds minimal value over the enum list.

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: 'Get recommended model' with a specific verb and resource. It distinguishes from sibling tools like 'get_recommended_prompt' by explicitly focusing on model selection (Sonnet vs Haiku) for different QA stages.

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 guidance is provided on when to use this tool versus alternatives such as 'get_recommended_prompt' or 'get_stage_config'. The description does not specify prerequisites or typical call contexts.

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