Compare Models

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by disclosing that the tool 'Works without any usage data (Day 0 value)' and specifying the return data (pricing, context windows, batch/cache support, cost savings). No contradictions 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?

The description is three sentences long, front-loaded with the core purpose, followed by specific outputs and a clear usage warning. No extraneous information.

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?

No output schema is provided, but the description adequately explains the return values (pricing, context windows, etc.). It also notes the Day 0 value. A minor gap: it could clarify that all parameters are optional and the default behavior, but the schema indicates optionality. Overall sufficient for the tool's complexity.

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% with clear descriptions for each parameter. The description does not provide additional parameter-level semantics beyond what the schema already offers. Baseline 3 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 verb 'compare' and the resource 'LLM model pricing and capabilities across providers'. It enumerates specific outputs (pricing per 1M tokens, context window sizes, etc.) and distinguishes from the sibling 'get_optimization_recommendations' by noting when not to use this tool.

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?

Explicit guidance is provided: 'Do NOT use for agent-specific recommendations — use get_optimization_recommendations which factors in actual usage patterns.' This clearly states when to avoid the tool and directs to an alternative.

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