list_models

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return data structure but lacks details on error handling, rate limits, authentication requirements, or whether the list is static or dynamic. For a read-only list tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 front-loaded with the core purpose and efficiently adds specific details about the return content and model count in a single, well-structured sentence. It avoids unnecessary elaboration, though it could be slightly more concise by integrating the model count into the main clause.

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 (0 parameters, no output schema, no annotations), the description is adequate but not fully complete. It covers the purpose and output semantics but lacks behavioral context like error cases or update frequency. For a basic list tool, this is minimally viable but leaves room for improvement in transparency.

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 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description appropriately does not discuss parameters, focusing instead on the output content. This meets the baseline for tools with no parameters, as it avoids redundancy.

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 specific action ('List all AI models'), identifies the resource ('available through LightningProx'), and provides distinguishing details about the return content ('model IDs, names, providers, and pricing') and scope ('19 models across Anthropic, OpenAI, Together.ai, Mistral, and Google'). This differentiates it from sibling tools like chat or check_payment.

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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context for usage, or comparisons with sibling tools. The agent must infer usage based solely on the tool name and description without explicit direction.

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