Measurement Uncertainty (gum / Iso 17025)

measurement-uncertainty-mcp

A Model Context Protocol server for GUM measurement uncertainty analysis. Built for ISO/IEC 17025 calibration labs, ISO 10012:2026 measurement management systems, and KOLAS / A2LA / UKAS accredited testing. No equivalent MCP server was found in a prior-art search (2026-07); that is not the same as being the first, and no claim of primacy is made here.

Compute Type A/B uncertainties, combined standard uncertainty u_c, effective degrees of freedom ν_eff via Welch-Satterthwaite, expanded uncertainty U with coverage factor k, Monte Carlo propagation per JCGM 101:2008, and apply pre-built KOLAS-ready uncertainty budgets — directly from Claude Desktop, Cursor, Windsurf, or any MCP client. No spreadsheet. No vendor lock-in. Standards-referenceable to JCGM 100:2008.

Now live on MCPize: https://measurement-uncertainty.mcpize.run

Why now — ISO 10012:2026

ISO 10012:2026 was published in February 2026 — the first revision since 2003. The standard introduces a stronger, risk-based approach and adds practical guidance on measurement uncertainty, Test Uncertainty Ratio (TUR), and decision rules with guard-banding (aligned with ILAC G8 and ISO 14253-1).

Most calibration labs are currently updating their Excel templates and uncertainty calculation workflows to match. This MCP server ships these primitives end-to-end and is designed to plug directly into the lab's chat-based AI workflow. The prior-art search below found no equivalent, but that is an absence of evidence, not proof of uniqueness. See Issue #6 for a planned KOLAS / A2LA / UKAS certificate auto-generation feature.

What this server does

Exposes 10 MCP tools that implement the Guide to the Expression of Uncertainty in Measurement (GUM, JCGM 100:2008) and its Monte Carlo supplement (JCGM 101:2008):

Tool	What it returns
type_a_uncertainty	Statistical uncertainty from a sample (n, mean, std, standard uncertainty, dof)
type_b_rectangular	Non-statistical uncertainty from a known half-width, uniform distribution (u = half_width / √3)
type_b_triangular	Same for triangular distribution (u = half_width / √6)
type_b_normal	Non-statistical uncertainty from a k-expanded value (u = U / k)
combine_uncertainty	Combined standard uncertainty from a list of components and sensitivities
welch_satterthwaite	Effective degrees of freedom from components
expanded_uncertainty	Expanded uncertainty with coverage factor k for a target confidence level
monte_carlo_propagate	JCGM 101 Monte Carlo propagation — output mean, standard uncertainty, shortest coverage interval, skewness, excess kurtosis. Use when the model is non-linear, inputs are non-Gaussian, or ν_eff is too small for k=2 to be valid
list_uncertainty_templates	Catalog of pre-built uncertainty budgets (CMM length, CD-SEM CD, DMM voltage, thermocouple, balance, OCD film thickness). Filters by industry / measurand
apply_uncertainty_template	Run a named template end-to-end — combine components, compute ν_eff, report U at target confidence. Accepts an overrides map so a template adapts to your setup without copy-paste

The Python library also ships a propagate(formula, estimates, components) helper for numerical uncertainty propagation through an arbitrary callable — not exposed as an MCP tool because callables don't serialize over JSON, but directly importable from measurement_uncertainty_mcp.math_kernel.

Who this is for

• Semiconductor equipment engineers doing CD-SEM, OCD, TEM, AFM, optical metrology. Wafer dispositions with proper uncertainty budgets, not hand-waved error bars.
• Calibration labs (KOLAS, A2LA, UKAS) that need uncertainty budgets in every certificate and spend hours in Excel on the same formulas.
• AI engineers building quality-control agents that must reason about measurement uncertainty before making a pass/fail call.
• Research groups publishing in journals that require GUM-compliant uncertainty reporting (AIP, IOP, Elsevier metrology titles).

Five example queries (paste these into Claude Desktop)

After connecting the server, you can ask things like:

1. CMM length calibration at 10 mm nominal

   "Apply the cmm_length_10mm template with my repeatability of 0.0003 mm from 10 readings. Report u_c and U at 95% confidence."

2. CD-SEM 45 nm linewidth uncertainty budget

   "Type A from these 20 CD-SEM measurements: [45.12, 45.08, 45.15, ... nm]. Combine with magnification calibration ±0.5 nm at k=2 and line-edge roughness ±0.4 nm at k=2. Report ν_eff via Welch-Satterthwaite."

3. DMM 10 V calibration with TUR check

   "Reference standard ±20 µV at k=2, DMM resolution half-width 1 µV, repeatability 3 µV from n=20. Compute u_c, expanded U at 95%, and the TUR if my tolerance is ±100 µV."

4. Type-K thermocouple at 100 °C, classroom budget

   "Apply thermocouple_k_100c template, override dmm_voltage to ±0.05 °C at k=2. What is the dominant uncertainty contributor?"

5. Non-linear ratio model — Monte Carlo propagation

   "Compute Y = (V × R) / (V + I) where V ~ N(10, 0.05), R ~ N(100, 0.5), I ~ N(0.1, 0.001). Run Monte Carlo with 200k trials and report the shortest 95% coverage interval."

Hosted endpoint

A hosted instance runs on MCPize for MCP clients that cannot run Python locally (sign-in required). Self-hosting from this repository gives the same tools.

Quickstart — hosted (no install)

Connect your MCP client directly to the live Cloud Run endpoint via MCPize. No Python, no dependencies, no config.

Claude Desktop / Cursor / Windsurf / Cline (one-line)

npx -y mcpize connect @kyb8801/measurement-uncertainty --client claude

Replace --client claude with cursor, windsurf, or cline as needed.

Claude Code CLI

claude mcp add --transport http "Measurement Uncertainty" https://measurement-uncertainty.mcpize.run

Manual config (claude_desktop_config.json)

{
  "mcpServers": {
    "Measurement Uncertainty": {
      "url": "https://measurement-uncertainty.mcpize.run"
    }
  }
}

Quickstart — local install (60 seconds)

For airgapped labs, offline use, or if you'd rather run locally:

# 1. Clone and install
git clone https://github.com/kyb8801/measurement-uncertainty-mcp.git
cd measurement-uncertainty-mcp
pip install -e .

# 2. Run the stdio server (will connect to any MCP client)
measurement-uncertainty-mcp

Then add this to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "measurement-uncertainty": {
      "command": "python",
      "args": ["-m", "measurement_uncertainty_mcp"]
    }
  }
}

Restart Claude Desktop, then ask:

"Compute the Type A uncertainty for these 10 CD-SEM measurements: 45.12, 45.08, 45.15, 45.11, 45.09, 45.13, 45.10, 45.14, 45.07, 45.12 nm. Report u, ν, and the expanded uncertainty at k=2."

Uncertainty-budget templates

Six starter templates ship in v0.2, covering the most commonly recurring budgets in calibration, teaching, and fab metrology:

Template id	Industry	Measurand	Nominal
cmm_length_10mm	calibration_lab	length	10 mm
dmm_dc_voltage_10v	calibration_lab	voltage	10 V
cdsem_linewidth_45nm	semiconductor	length	45 nm
ocd_film_thickness_50nm	semiconductor	thickness	50 nm
thermocouple_k_100c	university	temperature	100 °C
analytical_balance_500g	university	mass	500 g

list_uncertainty_templates previews component names, descriptions, and references. apply_uncertainty_template reads the raw component numbers, combines them via the GUM chain, and returns U at the target confidence. Users can pass an overrides map keyed by component name to adapt a template to their own measurement setup.

When self-hosting, set MCP_TIER=team in the server environment to enable apply_*.

Status

• Server skeleton
• Tool definitions (10 tools, JSON Schema)
• Math kernel
• MCPize deployment (live at measurement-uncertainty.mcpize.run, 2026-04-22)
• Public beta — 10 tools discovered and callable
• Cold-start optimization: scipy lazy import, cold p50 ~9× faster (#4)
• Monte Carlo uncertainty propagation (GUM Supplement 1 / JCGM 101:2008, 2026-04-22) (#2)
• Uncertainty-budget template library v1 — 6 seed templates (#3, 2026-04-23)
• Template library expansion: target 20 templates (pressure, torque, flow, dimensional, electrical)

Differentiation

Searched mcp.so, Smithery, and MCPize on 2026-04-17 for: uncertainty, metrology, calibration, GUM, measurement. Zero results on that date, in those registries, for those terms. That is the whole claim — it does not establish that no such server exists anywhere, and no market-size estimate is offered here.

Testing

pytest tests/        # kernel + monte carlo + templates — all 44 tests should pass

License

MIT — see LICENSE. Free for commercial use, attribution appreciated.