Chart Color Audit

io.github.socraticstaticPublished by GitHub account socraticstaticv0.2.5Updated Nov 1, 2026

CI-friendly accessibility audit for chart color palettes: WCAG contrast, CVD simulation, OKLab ΔE.

The publisher's own server: audit.domains - Domain Valuation

VerifiedSTDIODesktop onlyDeveloper ToolsData & Analytics

Overview

AI-generated overview

Audits chart color palettes for colorblind safety and WCAG contrast, letting an assistant measure series separation instead of guessing.

What you can do
  • Audit a pasted palette: simulate deuteranopia, protanopia, tritanopia and achromatopsia, measure pairwise OKLab separation and contrast against a background
  • Audit tokens from a config file: read series, semantic and text colors from CSS custom properties or design-tokens JSON and report named findings
  • Return per-vision minimum distance, closest pair, band and a pass or fail verdict with human-readable failure reasons
When to use it
Useful when choosing or reviewing dashboard and chart palettes and you want a measured answer to whether data series stay distinguishable for colorblind viewers and meet contrast floors, rather than an opinion.
Requirements
Runs locally over stdio via npx from the npm package chart-color-audit; no authentication, environment variables or headers are declared. The token audit reads a config file such as chartaudit.config.json pointing at CSS or design-tokens files. No network access is needed for the audit itself.
Before you install
Both tools are described as read-only, but the token audit reads files from the project it is pointed at, so only point it at repositories you are willing to expose to the assistant. It audits declared colors only; it does not render charts or check actual pixels.

Installation

In SourceWeft

  1. Open Chart Color Audit in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

chart-color-audit

Accessibility audit for chart color palettes. Answers one question with math: can everyone still tell your data series apart?

Plenty of tools check text contrast. This one checks the thing dashboards actually break: whether your palette survives colorblindness. It simulates deuteranopia, protanopia, and tritanopia with the published Machado, Oliveira & Fernandes 2009 matrices, measures pairwise separation in OKLab, checks WCAG 2.2 contrast against your background, and fails your build when a change quietly breaks any of it.

Four ways in. Pick yours.

you areyou wantgo to
a designer with a palette in handa verdict right now, nothing installed1 · Try a palette
a team with tokens in a repothe build to fail when a color change breaks accessibility2 · Gate your CI
an engineer building toolingthe audit as a typed function3 · Call it from code
working with Claude or another agentthe audit as an MCP tool4 · Wire it into an agent

1 · Try a palette (nothing to install)

bash
npx chart-color-audit --colors "#4E79A7,#F28E2B,#E15759" --bg "#fff"
chart-color-audit · 3 colors on #ffffff · mode: perceptual
  vision          min ΔE   closest pair   reading  · normal          14.6   2 ↔ 3        clearly distinct  · deutan          11.9   2 ↔ 3        clearly distinct  · protan          11.9   1 ↔ 3        clearly distinct  · tritan          10.3   2 ↔ 3        clearly distinct  · achromatopsia    5.2   1 ↔ 3        distinguishable
  contrast vs background: worst 2.42:1 (slot 2, needs ≥ 3:1)  ✗ slot 2 #f28e2b — 2.42:1
  FAIL — 1 finding:    · contrast: slot 2 (#f28e2b) is 2.42:1 vs. background (needs ≥ 3:1, WCAG 2.2 SC 1.4.11).

Those three colors separate cleanly under every colorblind simulation. The orange still fails: 2.42:1 against white, below the 3:1 floor for chart marks. Pale bars on white is one of the oldest dashboard sins, and this is it, caught in numbers.

Exit codes: 0 pass, 1 fail, 2 bad input. That is the whole CI contract.

Reading the output

ΔE is perceptual color distance (OKLab × 100). About 2 is the edge of what a human eye can tell apart.

readingmin ΔEmeaning
clearly distinct≥ 10survives a projector and a hallway glance
distinguishable≥ 2a careful reader separates the series
patterns carry identity< 2color alone is not enough; dash/shape must carry it
COLLISION≈ 0–1two series are the same color for these viewers

closest pair names the two slots (1-based) that came nearest. Fix those two, re-run, repeat.

CLI

bash
npx chart-color-audit --colors <list> --bg <color> [options]npx chart-color-audit [--config chartaudit.config.json]npx chart-color-audit mcp
flagwhat it does
--colors <list>comma-separated palette, any CSS color syntax: hex, hsl(), rgb(), oklch(), named, raw triples ("222 47% 6%")
--bg <color>background the marks render on
--mode <mode>perceptual (default) · redundant-encodings · strict (see Floors)
--config <path>config file, default ./chartaudit.config.json
--jsonfull result as JSON: verdict, failures[], perVision[], contrast[], semantic[], text[]
--version, --helpthe usual

2 · Gate your CI

Two files, no dependencies added.

Step 1. Put chartaudit.config.json in your repo root, pointed at the tokens you already have:

jsonc
{  // Where your tokens live: a .css file (custom properties) or a  // W3C design-tokens .json file. Optional; you can also write  // literal colors directly in the fields below.  "tokens": "src/index.css",
  // CSS selector whose block holds the variables. Optional, default ":root".  "selector": ":root",
  // REQUIRED. Your data-series colors, in slot order.  // Each entry is a literal ("#4E79A7"), a CSS variable name from the  // tokens file ("--chart-cat-1"), or a dotted path into design-tokens  // JSON ("chart.categorical.1").  "categorical": ["--chart-cat-1", "--chart-cat-2", "--chart-cat-3"],
  // REQUIRED. The background your marks render on.  "background": "--chart-bg",
  // Optional. Status colors, checked at 3:1 vs the background AND for  // collisions against the palette: the classic silent failure where a  // "positive" green KPI reads as data series 3 for deutan viewers.  // Verdict semantics: "low-contrast" and "collision" FAIL the audit;  // "cvd-risk" (collides with a slot only under CVD simulation) is  // REPORTED but does not fail — semantic roles usually carry redundant  // cues (position, icon, label). Treat cvd-risk as a review prompt.  "semantic": { "positive": "--chart-positive", "muted": "--chart-muted" },
  // Optional. Tokens rendered as UI text (status labels, captions, table  // numbers). Text needs 4.5:1 (SC 1.4.3), not the 3:1 mark floor.  // Reusing mark tokens as text is the most common failure I have shipped:  // one measured sweep found 111 instances of it in a single app.  "text": { "positive": "--chart-positive-text" },
  // Optional. Floors preset, and per-floor overrides on top of it.  "mode": "perceptual",  "floors": { "minContrast": 3, "minTextContrast": 4.5 }}

Step 2. Add the workflow:

yaml
# .github/workflows/chart-colors.ymlon: [push, pull_request]jobs:  chart-colors:    runs-on: ubuntu-latest    steps:      - uses: actions/checkout@v4      - run: npx chart-color-audit

Done. A token edit that breaks a floor now exits 1 with named findings, in the PR, before anyone screenshots anything.

Prefer a pinned dependency? npm i -D chart-color-audit and call it from a package script. Same behavior.

The famous-palette test

bash
npx chart-color-audit --colors "#4E79A7,#F28E2B,#E15759,#76B7B2,#59A14F,#EDC948,#B07AA1,#FF9DA7,#9C755F,#BAB0AC" --bg "#fff"

That is Tableau 10, one of the most-shipped chart palettes on earth, on a white dashboard:

  ✗ deutan           0.7   3 ↔ 5        COLLISION  ✗ protan           0.9   4 ↔ 10       COLLISION  ✗ achromatopsia    1.6   2 ↔ 4        COLLISION  ...  FAIL — 8 findings

Its red and green sit ΔE 0.7 apart under deuteranopia simulation. Roughly 1 in 12 men has red-green color vision deficiency; for most of them, series 3 and series 5 on that chart are the same color. Nothing in code review catches this. Math does.

3 · Call it from code

bash
npm i chart-color-audit
ts
import { audit } from "chart-color-audit";
const result = audit({  colors: ["#4E79A7", "#F28E2B", "#E15759"],  background: "#ffffff",  // optional: semanticRoles, textRoles, mode, floors});
result.verdict;   // "pass" | "fail"result.failures;  // human-readable reasons, empty on passresult.perVision; // per simulation: minDeltaE, closestPair, band

Fully typed. Two data dependencies (culori for color math, the MCP SDK for the server). No DOM, no network.

4 · Wire it into an agent (MCP)

Let an agent audit colors mid-conversation:

bash
claude mcp add chart-color-audit -- npx chart-color-audit mcp

Two read-only tools: audit_palette (paste colors) and audit_tokens (point at a config). Ask "is this palette colorblind-safe on white?" and the answer comes back with measurements instead of vibes.

Listed in the official MCP Registry as io.github.socraticstatic/chart-color-audit and on Glama.

Floors

modefails whenfor
perceptual (default)any pair lands below ΔE 2, the edge of human perception, under any simulation; or contrast < 3:1palettes where color alone carries identity
redundant-encodingsoutright collision only (ΔE < 1 normal / < 0.1 CVD)design systems pairing every color with a dash/decal/shape channel
strictbelow ΔE 10 normal / ΔE 4 CVDcontrol rooms, projectors, hallway glances

Text tokens are checked at 4.5:1 in every mode. Changing default floors is a major version, always: a CI gate that tightens defaults in a patch release breaks builds and trust.

Honest scope

Tokens in, findings out. This audits declared palette colors. It does not render charts, screenshot pixels, or crawl pages. Contrast checkers for text exist in plenty; what this adds is the part I could not find anywhere else: CVD-simulated series separation, as a build gate and an MCP tool.

Where this came from

The engine was extracted from a chart color system whose own history proves the point: an "accuracy fix" commit once replaced the correct deuteranopia matrix with confident, plausible, fabricated values, and review passed it. Only a pinned regression test would have caught it, so this package pins the matrices against the published paper in its test suite, permanently.

The full story: docs/POSTMORTEM.md: nine weeks of a wrong matrix, the AI-co-authored commit that forged it, and why the math now checks the math.

Consulting

I audit dashboards and design systems for exactly these failures. 30 years of UX practice, and the math above to prove findings instead of arguing them. [email protected]

MIT © Micah Boswell

Source: README.md at commit 5624266

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

2
  1. v0.2.5LatestOct 4, 2026
  2. v0.2.4Sep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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