List All Models

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds valuable context beyond annotations: it reveals the scale ('60+ models'), mentions that results include 'pricing data', and specifies that model IDs should be used with other tools—useful behavioral details not captured in 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 perfectly structured and concise: it starts with a clear purpose statement, followed by well-organized sections for Args, Returns, and Examples. Every sentence adds value—no wasted words—and information is front-loaded with the core functionality stated first.

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 low complexity (one optional parameter), rich annotations, and 100% schema coverage, the description is nearly complete. It covers purpose, usage, parameter examples, and output format. The only minor gap is lack of an output schema, but the description adequately describes the return format. It effectively complements the structured data.

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 description coverage is 100%, with the schema fully documenting the optional 'provider' parameter. The description adds minimal value beyond the schema: it provides example provider values ('OpenAI', 'Anthropic', 'Google') and shows usage examples, but doesn't explain parameter semantics beyond what the schema already states. This meets the baseline of 3 for high schema coverage.

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 ('List') and resource ('all available LLM models with pricing data'), and explicitly distinguishes this tool from siblings by specifying it returns 'all models with IDs, names, and providers' and noting 'Use model IDs with other tools'—differentiating it from compare_models, estimate_cost, find_cheapest, get_model_pricing, and list_providers.

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 provides explicit guidance on when to use this tool vs alternatives: it specifies 'List all available LLM models with pricing data' and includes examples showing both unfiltered and provider-filtered usage. It implicitly contrasts with siblings like list_providers (which lists providers only) and get_model_pricing (which gets pricing for specific models).

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