List Available LLMs

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful context by stating the data source (Okareo registry) and the shape of entries (name, display name, provider). This goes beyond the basic safety profile and explains how the output should be consumed.

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 three sentences, front-loaded with the core purpose, and every sentence earns its place. It avoids fluff and directly states what, why, and how to use the result. Excellent conciseness.

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 strong annotations and an output schema, the description is complete. It covers the registry source, the purpose (registration), and the result fields. No additional behavioral or usage context is needed.

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 input schema is trivial. Per rubric, a baseline of 4 is appropriate. The description adds no parameter details (none exist), but it does explain the output usage, which is the only semantically relevant information needed.

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 function: 'Browse available LLMs from the Okareo registry' and 'Returns all LLMs that can be used when registering a generation model.' It distinguishes this from sibling tools like list_generation_models by explicitly tying it to the registry and the register_generation_model workflow.

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?

Provides explicit guidance: 'Use a model_name from this list when calling register_generation_model.' This tells the agent exactly when to use this tool. It doesn't discuss when not to use it or contrast with alternatives, but the usage context is clear.

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