list_models

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

Annotations already provide readOnlyHint and openWorldHint, so the safety and scope are known. The description adds the non-obvious trait that the call is free, and 'every' reinforces the open-world nature. No contradiction; meaningful context added beyond 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?

Two sentences, each earning its place: the first states what the tool lists, the second states cost and usage guidance. No unnecessary words, information is front-loaded.

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 simple zero-parameter list tool with an output schema and strong annotations, the description covers purpose, cost, and usage guidance. Nothing material is missing; the output schema handles return structure details.

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 zero parameters, so the baseline is 4. The description compensates by listing the output fields (id, category, provider, per-call price), giving the agent a preview of what to expect even though an output schema exists.

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 'List' with a clear resource 'GlianaAI model' and scope 'every', enumerating the fields returned (id, category, provider, per-call price). It explicitly separates this tool from siblings by positioning it as a prerequisite before get_price/generate.

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?

'Use this to pick a model before get_price/generate' explicitly states when to use the tool and implies the ordering relative to alternatives. The cost note 'Free — no payment' also guides the agent on when it's safe to call.

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