route_model

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

No annotations provided, so description carries full burden. It mentions filtering criteria and returns, but does not disclose failure behavior (e.g., if no model meets constraints), side effects, auth needs, or how capability is determined. Significant gaps for a recommendation tool.

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?

Two sentences, front-loaded with purpose. No wasted words. Efficient and clear.

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?

With 5 parameters and an output schema, description covers the core idea but misses details on some parameters and behavioral aspects like empty results. Adequate but not comprehensive.

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 has 5 parameters with 0% description coverage. Description explains min_quality (1-10, default 7) and max_cost_per_1k, but omits prefer_free and require_long_context. Estimated_tokens is required but only implied. Adds meaning but incomplete.

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?

Description states 'Recommend the cheapest capable model for a task.' This is a specific verb+resource pair. Among siblings (analyze_context, cache, etc.), none offer model recommendation, so it is clearly distinguished.

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?

Description implies usage when selecting a cost-effective model, but does not explicitly state when to use this tool versus alternatives, nor when not to use it. Missing guidance on when to use siblings like analyze_context or savings_report.

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