Ask an AI model

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

No annotations are provided, so the description carries the burden. It discloses cost with 'Billed per token from the credit balance', which is relevant context. However, it omits information about response format, rate limits, or any side effects beyond billing, leaving gaps in transparency.

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, front-loaded with the action and scope, and includes billing and model discovery without any fluff. Every sentence serves a purpose.

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?

With 5 parameters and no output schema, the description gives the core purpose and cost but leaves return format and usage boundaries implied. It does not describe the response structure or when to prefer other tools, making it only moderately complete for an agent.

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?

Schema description coverage is 60%, so the baseline is 3. The description adds no extra detail about system, max_tokens, or temperature, though it does clarify 'model slugs' indirectly via list_models. The schema already covers model and prompt adequately, but the uncovered parameters remain unaddressed.

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?

Description uses a specific verb 'Send a prompt' and names the resource 'any of the 120+ models on ApexApi'. It clearly differentiates from siblings like generate_image and generate_speech by focusing on prompt-based text generation. The title 'Ask an AI model' also aligns well.

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 explicitly instructs to 'Use list_models to discover model slugs', which is a useful prerequisite. It does not provide exclusions or mention alternatives like extract_structured, but the primary use case is clear and the guidance is sufficient for basic selection.

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