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

With no annotations, the description must disclose behavioral traits itself. It only states that the tool lists models with pricing and capabilities, without mentioning read-only behavior, rate limits, pagination, or side effects. This is a minimal disclosure for a list operation.

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 that directly conveys the core function. It is front-loaded and contains no unnecessary words, achieving good conciseness.

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?

For a tool with no parameters and no output schema, the description provides enough context about what the tool returns (a list of models with pricing and capabilities). It could be more explicit about being a read-only overview, but the simplicity of the tool makes this adequate.

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?

The tool has zero parameters, so the baseline is 4. The description does not need to explain parameters because there are none. It adds no parameter-specific meaning but also does not need to.

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 lists all supported AI models with pricing and capabilities. The verb 'list' and resource 'supported AI models' establish a specific purpose that distinguishes it from siblings like compare_models and route_model.

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?

Usage guidance is implied but not explicit. The description indicates the tool is for listing all models, but it does not mention when to use this versus comparing or routing models. No alternatives or exclusion criteria are provided.

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