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

Annotations already establish readOnlyHint=true and openWorldHint=false, and the description adds useful behavioral context: the tool costs $0.005 USDC on Base via x402, covers roughly 3,300 models, and sorts results by input price. It does not discuss pagination or error behavior, but the payment and data-scope details are meaningful additions.

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 definition is front-loaded and appropriately compact, leading with the core lookup purpose and return values. The long parenthetical model/provider lists are slightly dense but still serve to communicate coverage rather than adding fluff.

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 no output schema, the description carries the return-value burden and does so clearly by naming the token price fields and context window. For a three-parameter read-only lookup, it supplies enough context about coverage, sorting, filtering, and cost.

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 100%, so all three parameters are already documented in the schema. The description mentions filtering by model and provider and sorting by input price, but it adds little parameter-level syntax or meaning beyond the schema and does not explain the limit parameter.

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 states a specific verb/resource combination ('LLM API pricing lookup') and names the exact returned fields: USD per 1M input, output, cached-input tokens, and context window. It also distinguishes the domain from sibling tools like ai_gpu_prices by making clear this is LLM API pricing, not GPU pricing.

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 says to filter by model and provider and mentions the x402 cost, but it does not explicitly say when to use this tool instead of ai_gpu_prices or other sibling tools. Usage is implied by the purpose rather than stated with alternatives or exclusions.

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