list_models

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

With no annotations provided, the description carries the full burden. It states a read-only 'list' operation, which clearly implies no side effects. It also specifies the scope ('from your LiteLLM instance'). While it does not explicitly discuss authentication or response format, the behavior is straightforward and transparent for a simple list operation.

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 a single, concise sentence that gets straight to the point. It is front-loaded with the action and resource, containing no filler or redundant information. Every word 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 low complexity (zero parameters, no output schema), the description is complete. It clearly explains what the tool does (lists available models) without needing to describe return values or edge cases. The context is fully covered.

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, and the input schema is empty. The baseline for 0 parameters is 4, and the description adds no parameter information because none exist. There is no need for further elaboration.

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: 'List all available models from your LiteLLM instance'. It uses a specific verb ('list'), identifies the resource ('available models'), and scopes it to the user's LiteLLM instance. This distinguishes it from siblings like model_info, which likely provides details on a specific model, and chat_completion, which handles completions.

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 implies usage—when you need to see all available models, use this tool—but it does not explicitly mention alternatives or when not to use it. No exclusions are given, and sibling tools like model_info are not referenced. The intent is clear but not explicitly differentiated.

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