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laszlopere/mcp-gnu-units

by Various

Unit converter for AI agents, powered by the GNU units database — convert 3000+ units of measurement, evaluate compound unit expressions, reduce to SI base units, dimensional analy

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MCP

laszlopere/mcp-gnu-units

Added 4 July 2026

#gnu-units #mcp #mcp-server #measurement #model-context-protocol #unit-conversion #units

Overview

A unit converter for AI agents that uses the GNU units database. It can convert over 3000 units of measurement, evaluate compound unit expressions, reduce to SI base units, and perform dimensional analysis. The tool operates offline and produces deterministic results.

Best for

Best for
Developers needing deterministic offline unit conversion in AI agent pipelines

Use cases

  • Converting units in automated data pipelines
  • Validating dimensional consistency in engineering calculations
  • Providing offline unit conversion for AI agent workflows

Notes

A unit converter for AI agents that uses the GNU units database. It can convert over 3000 units of measurement, evaluate compound unit expressions, reduce to SI base units, and perform dimensional analysis. The tool operates offline and produces deterministic results.

0 stars on GitHub. Last updated 2026-07-03. Licensed GPL-3.0.

Use cases

  • Converting units in automated data pipelines
  • Validating dimensional consistency in engineering calculations
  • Providing offline unit conversion for AI agent workflows

Pros

  • Offline operation eliminates dependency on external APIs
  • Deterministic results ensure reproducibility
  • Covers over 3000 units from the GNU units database

Cons

  • Zero stars on GitHub indicates minimal community adoption
  • Limited documentation or support may hinder integration
  • Requires Python environment and MCP setup

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Offline operation eliminates dependency on external APIs
  • Deterministic results ensure reproducibility
  • Covers over 3000 units from the GNU units database

Cons

  • Zero stars on GitHub indicates minimal community adoption
  • Limited documentation or support may hinder integration
  • Requires Python environment and MCP setup