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

Annotations already declare read-only, idempotent, non-destructive behavior, so the description's burden is lower. It adds valuable context about freshness, the geographic scope being EU plus UK, and specific excluded countries, which helps agents anticipate results and avoid false expectations.

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 sentences with no filler: the first sentence front-loads the tool's output content, and the second provides actionable usage guidance with concrete examples. Every part 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?

For a zero-parameter, no-output-schema tool, this description is complete: it lists the returned data elements, notes freshness, defines the covered region, and gives explicit exclusions. An agent has everything needed to decide when to call this tool and what to expect.

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, so there is no parameter ambiguity to address. The description appropriately focuses on what the tool reports rather than input semantics, and no additional parameter explanation is needed.

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 identifies this as a coverage/scope tool for fuel-price data and enumerates exactly what it returns: countries, newest and oldest week, observation count, and fuel types. It is easily distinguished from sibling tools like fuel_price and fuel_price_history because it describes metadata coverage rather than price values or historical series.

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 agents to use this tool to check whether a country is in scope before asking about it, which is strong usage guidance. It also gives concrete examples of out-of-scope countries, but it does not name alternative tools or explicitly say when not to use it, so it slightly misses the top tier.

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