Cheapest fuel near a location

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It truthfully discloses that US results are only official regional averages, not station-level prices, and it reveals the smart=true caveat that the cheapest sign is not always the cheapest tank. This adds meaningful behavioral nuance beyond the input schema, though it does not mention request limits or exact response format.

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 compact at two sentences, with the primary purpose front-loaded. The second sentence earns its place by explaining a critical behavioral nuance (smart=true) that would otherwise be ambiguous. No filler or repetition exists.

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 medium complexity and lack of output schema, the description adequately covers key selection factors: country-specific data granularity, required location inputs, and smart mode semantics. It does not explicitly state the return format (e.g., whether a list or a single result is returned), but the description is sufficient for an agent to decide when to invoke it and with what parameters.

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 input schema already provides 100% parameter coverage, including descriptions for lat/lon (required for station-level countries), region (US state code), and country (ES/FR/IT station-level vs US regional). The description's added detail about data sources and smart=true behavior is useful but largely paraphrases or reinforces what the schema already documents, so it only marginally raises the baseline.

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 opens with 'Cheapest fuel for a location and grade,' a specific verb+resource statement that clearly identifies the tool's function. It further differentiates station-level pricing (Spain, France, Italy) from regional averages (US), and introduces the smart=true refinement, making it distinct from any other possible fuel-related tool.

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 clear context on when station-level versus regional pricing applies, and explicitly explains what smart=true does ('weighs the pump price against the fuel and time spent on the detour'). It stops short of naming alternative tools or saying 'when not to use,' but the sibling tools are unrelated, so the lack of explicit alternatives is not a major gap.

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