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units_convert

Convert between units of length, mass, volume (incl. US cups and gallons), temperature (c, f, k), speed, area, data size (kB vs KiB), time, energy and pressure. Example units: km, mi, lb, kg, floz, ml, mph, kn, acre, gib, kwh, psi. Use when exact conversions matter. Costs $0.001 in USDC on Base per successful call, paid with x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesUnit to convert to
fromYesUnit to convert from
valueYesThe amount

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations only give the generic safety profile (readOnlyHint=false, idempotentHint=false, openWorldHint=true, destructiveHint=false). The description adds genuinely useful behavioral context beyond them: a metered cost of $0.001 USDC on Base via x402 per successful call, plus precision nuances like kB vs KiB and US cups/gallons. It does not say what happens on an unsupported unit (error vs. fuzzy match) or whether a failed call is still billed.

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?

Front-loaded with the capability and unit families, then usage, then payment terms — a sensible priority order with no filler sentences. It is somewhat list-dense, but each element (unit families, examples, cost) carries distinct information.

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?

With no output schema and full schema coverage on inputs, the description covers capability, input vocabulary, and cost. The remaining gap is the return contract — numeric vs. formatted string, rounding precision, and error behavior for unknown units — which an agent calling this blindly would benefit from knowing.

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% and the schema already labels value/from/to, so the baseline would be 3. The description goes further by supplying real example unit tokens (km, mi, lb, kg, floz, ml, mph, kn, acre, gib, kwh, psi) that tell the agent what string format `from`/`to` accept — meaningful semantics the schema does not provide.

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?

States a specific verb+resource (unit conversion) and enumerates the exact quantity families covered, with concrete example units. No sibling tool in the list performs conversion, so the scope is unambiguous and immediately distinguishable.

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?

"Use when exact conversions matter" gives a clear selection context, and the paid nature implies when not to use it carelessly. It stops short of naming alternatives or stating exclusions (e.g., currency conversion is a separate `fx` tool — that routing is not mentioned).

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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