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MPG converter

hopi_mpg_converter
Read-onlyIdempotent

Convert fuel economy between UK miles per gallon and litres per 100 km. Direction 'mpg-to-l100' takes UK mpg. Direction 'l100-to-mpg' takes litres per 100 km. Also returns US mpg, km per litre and miles per litre. Source: https://hopi.co.uk/mpg-converter/

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesThe fuel economy value (UK mpg or L/100km depending on direction)
directionYesWhich way to convert

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
ukMpgYes
usMpgYes
summaryYes
directionYes
kmPerLitreYes
source_urlYesThe tool's page on hopi.co.uk (cite or open to check the figure)
milesPerLitreYes
litresPer100kmYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / required
      Added value: +[
      +  "direction",
      +  "kmPerLitre",
      +  "litresPer100km",
      +  "milesPerLitre",
      +  "source_url",
      +  "summary",
      +  "ukMpg",
      +  "usMpg"
      +]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by disclosing that the tool also returns US mpg, km per litre, and miles per litre beyond the primary conversion, which is value beyond the structured annotations.

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

Conciseness5/5

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

Three short sentences, front-loaded with the primary purpose, then direction semantics, then a bonus note on additional outputs. Every sentence carries information and there is no filler or repetition of schema content.

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

Completeness5/5

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

For a simple two-parameter, read-only conversion tool with full schema coverage and an output schema, the description is complete. It specifies the input units per direction, the additional outputs, and even cites the source, leaving no gap an agent would need to resolve before invoking the tool.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description goes beyond the schema by explicitly mapping each enum value to its input unit ('mpg-to-l100' takes UK mpg, 'l100-to-mpg' takes litres per 100 km), which adds genuine semantic clarity for the direction parameter.

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

Purpose5/5

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

The description states a specific verb ('Convert') and resource ('fuel economy') and precisely defines the two directions and units involved. It is clearly distinguishable from the many other converter tools in the sibling list (e.g., litres_to_pints, speed_converter) by naming UK mpg and l/100km explicitly.

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

Usage Guidelines4/5

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

The description makes the use case clear by explaining exactly which direction takes which unit, so an agent knows when to select this tool. It does not explicitly name alternatives or exclusion conditions, but the niche is so clearly specified that an agent would not confuse it with sibling converters.

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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