List providers

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

With no annotations, the description carries the behavioral burden. It accurately conveys a read-only, comprehensive listing operation and states that each provider is returned with its model count. However, it does not disclose output format details, ordering, or any potential pagination, though the simple no-param nature keeps this a mild gap.

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, no filler. The core behavior and examples come first, and the usage guidance follows immediately. Every sentence adds value.

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 no-parameter listing tool, the description explains what is listed, what data is included, and when to use it. It could explain the exact return shape or ordering, but the tools's simplicity means this is nearly complete.

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 schema already fully documents everything and there is no ambiguity. The description adds no parameter-level detail, but none is needed; baseline 4 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 uses a specific verb and resource: it lists every provider in the models.dev catalog and mentions the included model count. The examples (anthropic, openai, google) and the contrast with narrowing via find_models make it clearly distinct from siblings like get_provider and find_models.

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?

It clearly states when to use the tool: to see what's available before narrowing with find_models. It does not explicitly say when not to use it or name alternative tools, but the context is clear enough for an agent to route correctly.

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