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tresor4k

macalc

calculate_fuel_economy_conversion

Convert fuel economy between L/100km, mpg (US/UK), and km/L for comparing car efficiency across regions. Input value, source unit, and target unit.

Instructions

Convert between fuel economy units: L/100km, mpg-US, mpg-UK, km/L. Use for car comparisons across regions. Inputs: value, from-unit, to-unit. Returns converted economy. See list_bundles for related 'conversions' calculators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesFuel economy value to convert
from_unitYesSource unit of fuel economy
to_unitYesTarget unit of fuel economy

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoComputed result. Object whose fields depend on the tool (e.g. {tax, marginal_rate, brackets} for tax tools, {volume_l, gallons} for volume tools).
formulaNoHuman-readable formula or method used (e.g. "I=P·r·t", "Magnus formula").
sourceNoAuthoritative source for the rule or formula (e.g. "Article 197 CGI", "NF DTU 21").
reference_urlNoLink to a calcul2 page documenting the calculation in detail.
Behavior3/5

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

No annotations are provided, so the description must fully convey behavioral traits. It states 'Returns converted economy' but omits details like rounding, precision, or error handling for edge cases (e.g., value=0). The description is adequate for a simple conversion but lacks depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at three sentences, with the purpose and use case front-loaded. The third sentence listing inputs and outputs is somewhat redundant given the schema, but overall the structure is clear and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple conversion operation with an output schema, the description provides sufficient context: what it converts, when to use, and where to find related tools. It could mention the conversion formulas for transparency, but the presence of an output schema mitigates the need.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the baseline is 3. The description repeats the parameter names without adding further meaning (e.g., allowed values, constraints). It does not clarify that from_unit and to_unit must be from the provided enum, which is already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool converts between fuel economy units (L/100km, mpg-US, mpg-UK, km/L), but does not differentiate from the sibling tool 'convert_fuel_consumption' which likely performs the same conversion. A brief distinction would improve clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description suggests using this tool for 'car comparisons across regions', providing useful context. However, it does not specify when not to use it or direct to alternatives. The mention to see list_bundles for related calculators is vague and not directly actionable.

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

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