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

Annotations already declare read-only, idempotent, closed-world, and non-destructive behavior. The description adds useful context about the fields returned, but does not address pagination, result completeness, sorting, or whether all models are always included.

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 definition is a single well-structured sentence with the resource front-loaded. Every phrase contributes to describing what is returned, with no redundant wording.

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 zero-parameter listing tool with no output schema, the description adequately enumerates the key returned fields and annotations cover the safety profile. Minor gaps remain around pagination and ordering, but the definition is largely complete for this 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?

There are no input parameters, so the baseline is 4. The description does not need to explain parameter meanings.

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 that it returns every model with price per million tokens, context length, providers, and OpenUncensored Score rank. It distinguishes a bulk listing from a single-model lookup only implicitly through 'Every model,' but does not explicitly name siblings like get_model or get_rankings.

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 is implied by 'Every model,' suggesting this is for broad model discovery rather than retrieving one model. However, the description gives no explicit guidance on when to use this versus get_model or get_rankings.

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