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

No annotations are provided, so the description carries the full burden. It mentions inputs and that it returns '{input}', but this is ambiguous—it does not clarify that the output contains the converted result. No details on validation, limits, or error handling are provided.

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 three sentences, each providing distinct information: purpose, use case, inputs/returns, and a pointer to related tools. No unnecessary words.

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 simple conversion tool with 100% schema coverage and an output schema, the description is mostly adequate. However, the return description ('{input}') is ambiguous and does not clearly state that the converted result is returned. The pointer to list_bundles adds some completeness.

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 coverage is 100%, so a baseline of 3 applies. The description lists the three parameters (value, from, to) which repeats the schema, and adds context ('car comparison across regions'), but does not add detailed meaning beyond what the schema provides.

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 the tool converts fuel consumption between four specific units and provides a use case ('car comparison across regions'). It distinguishes itself from sibling calculators like calculate_fuel_consumption by focusing on unit conversion.

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 gives a use case ('Use for car comparison across regions') but does not explicitly state when not to use this tool or list alternatives. It directs to list_bundles for related calculators, but the guidance is implied rather than explicit.

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