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MCPFax Public-Data Utility API

Unit conversion

v1_unit_convert
Read-onlyIdempotent

Unit conversion: Convert between units (length, mass, volume, speed, area, data, time, pressure, energy, temperature, angle). Source: deterministic edge compute. $0.005 per call · GET /v1/unit-convert

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesTarget unit. Example: 'km'.
fromYesSource unit. Example: 'mi'.
valueYesValue to convert. Example: '100'.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • addedInput schema / properties / from / examples
      Added value: +[
      +  "mi"
      +]
    • addedInput schema / properties / to / examples
      Added value: +[
      +  "km"
      +]
    • addedInput schema / properties / value / examples
      Added value: +[
      +  100
      +]
  2. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds useful context beyond annotations by noting 'deterministic edge compute,' a per-call cost, and the HTTP GET endpoint, which helps agents understand reliability and side effects without contradicting annotations.

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 short and front-loads the core purpose followed by concise metadata. The opening phrase 'Unit conversion:' is slightly redundant with the title, but the rest of the description is compact and informative.

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?

For a simple three-parameter conversion tool, the description is adequate: it names the unit categories, confirms deterministic behavior, and full schema documentation covers inputs. It does not specify valid unit code formats or return structure, but this is not critical given the open schema and examples.

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?

Schema description coverage is 100%, with each parameter (value, from, to) already having descriptions and examples. The tool description adds no meaningful parameter-level detail beyond the schema, so the baseline score of 3 applies.

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 units and enumerates the supported domains (length, mass, volume, speed, area, data, time, pressure, energy, temperature, angle). It is a specific verb+resource description, though it does not explicitly distinguish itself from the sibling v1_fx currency conversion tool.

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 listed unit categories imply when the tool is relevant, but the description provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives such as v1_fx for currency conversion, leaving some routing decisions to inference.

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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TDQS

A3.7/5.0
Disambiguation5/5

Each tool maps to a distinct public data source and lookup type, from weather and geocoding to legal codes and vehicle VINs. Even adjacent tools like weather vs. weather_alerts or geocode vs. reverse_geocode are clearly separated by resource and direction.

Naming Consistency4/5

All tools share a consistent v1_ prefix and snake_case resource naming, making the pattern predictable. Minor inconsistencies exist: most names are noun phrases (v1_country, v1_stock_quote) while a few are verb-led (v1_geocode, v1_validate_email, v1_unit_convert), and some abbreviations like v1_cfr and v1_lei are less descriptive.

Tool Count2/5

At 31 tools, the surface is large and will strain agent tool-selection, even though each tool is individually useful. The broad 'public data utility' scope explains the count, but the calibration threshold of 25+ tools indicates an oversized set for practical use.

Completeness4/5

For a lookup-oriented public data utility, the set covers a wide range of common needs—weather, finance, location, legal/medical codes, domain/network, and conversions—without dead ends. It lacks some obvious public data categories (e.g., web search, population/census, news) and enumeration endpoints, but agents can work around these gaps.

Resources