list_models

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

No annotations are provided, so the description bears full responsibility. It clearly states the tool is free, requires no payment, and is idempotent—safe to call repeatedly. It also describes the output format. This fully informs the agent of behavioral traits.

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 concise at 4 sentences, front-loads the purpose, and uses a clear example of the output format. Every sentence adds essential information with no wasted words.

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?

Despite no output schema, the description provides a full example of the return format. It covers the purpose, usage, filtering, isDefault behavior, and safety aspects. Given the tool's simplicity (one optional parameter), the description is 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?

Schema coverage is 100% with the category parameter described. The description adds the context that filtering narrows results 'to a specific tool', which clarifies the enum values correspond to tool names, adding value beyond the schema.

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's purpose: 'Discover available AI models with numeric IDs, tier labels, capabilities, and per-call pricing in sats.' It distinguishes from sibling tools like create_payment and get_model_pricing by specifying its role in selecting a modelId and noting it is free and idempotent.

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 explicitly advises calling this tool before create_payment to find the right modelId, and explains how the isDefault flag works. It also mentions filtering by category. While it doesn't explicitly compare to other tools like get_model_pricing, the guidance is clear and practical.

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