list_models

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

With no annotations, the description carries the full burden, and it adequately discloses that this is a read-only listing operation. It specifies what is returned (type, training metadata, scores), which is sufficient behavioral information for a non-destructive tool with no side effects.

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 two concise sentences with no wasted words. The first sentence front-loads the action and result; the second adds practical guidance. Every sentence earns its place.

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?

Given the tool's simplicity (zero parameters, an output schema exists, and a clear listing purpose), the description is complete. It provides purpose, usage guidance, and expected content without needing to explain return values since the output schema covers them.

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 score of 4 applies. The description appropriately does not attempt to document parameters that do not exist, and the empty schema already provides complete coverage.

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 trained models with their type and training metadata, using a specific verb ('List') and resource ('trained models'). This distinguishes it from sibling tools like train_model or evaluate_model, leaving no ambiguity about its function.

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 provides clear usage context: 'Check which models have been trained and their training scores before deciding next steps.' This tells the agent when to use the tool, though it does not explicitly name alternatives or when-not-to-use scenarios, keeping it just short of a 5.

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