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

Annotations already declare read-only and non-destructive behavior, so the description adds meaningful context about the specific information returned (coverage, freshness, endpoints, pricing). No contradictions and no missing side-effect warnings needed.

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?

A single concise sentence that front-loads the key content (coverage, freshness, endpoints, pricing). Every element is informative and there is zero redundancy.

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 no-parameter info tool with a rich output schema and strong annotations, the description adequately conveys the tool's purpose and scope. The complexity is low, and the description covers all necessary context.

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, and schema coverage is 100%. The description is not required to explain parameters, and no parameter information is needed. A baseline of 4 is appropriate.

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 what the tool provides: coverage, freshness, endpoints, and x402 pricing for the dataset. This distinguishes it from sibling tools like check_entry_requirements and evaluate_trip, which focus on actual visa checks and trip evaluation rather than dataset metadata.

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 implies when to use this tool (to learn about dataset coverage/freshness or pricing) and clearly separates it from siblings. It lacks explicit 'when not to use' or named alternatives, but the context is clear enough for an agent to choose correctly.

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