CanaryUsers MCP Server
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@CanaryUsers MCP ServerScan https://myapp.com for UX issues"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CanaryUsers — MCP Server
Give your AI coding assistant eyes for UX: a flock of AI users tests your deployed app and reports where real people would bail — each with a concrete fix.
CanaryUsers sends a flock of behaviorally‑diverse AI users through your deployed web app (a live or preview URL) and reports exactly where real people get stuck — confusing UX, broken mobile layouts, dead‑end conversion paths, and visual glitches — each with a concrete, AI‑ready fix you can apply in the same chat.
This is the public home for the CanaryUsers remote MCP server: manifest, install instructions, and docs. The product itself is hosted (closed source) — there's nothing to clone or run; you connect to the live endpoint. See docs/WHY.md for why this repo exists and docs/HOW-IT-WORKS.md for how the server works.
Registry name:
ai.canaryusers/canaryusersEndpoint (remote, Streamable HTTP):
https://www.canaryusers.ai/api/mcpAuth:
Authorization: Bearer <your CanaryUsers token>Setup / get a token: https://www.canaryusers.ai/mcp
What it does
You point it at a URL; a flock of AI personas actually renders and explores the page like real users, then hands back prioritized, fix‑ready findings — not a vague score. Two modes:
Quick (default) — a fast static check. Free.
Deep — renders the page, inspects it visually on desktop + mobile (catches mobile breakage and layout issues), and clicks through key flows like signup / checkout / onboarding (~60–90s, uses credits).
Related MCP server: UserFlow MCP
When to use it
Right after you ship or deploy a UI change (it scans the deployed result, not your source).
Before a launch, to pressure‑test the first‑run experience.
To check a specific high‑stakes flow: signup, checkout, onboarding, pricing, or a landing page.
When you suspect mobile breakage or a conversion drop‑off you can't see in analytics.
Why not Lighthouse or analytics?
Lighthouse grades your HTML; analytics charts what already happened. CanaryUsers sends users and tells you, in plain language, where they'd give up and why — before real ones do.
Tools
Tool | What it does |
| Run a UX scan on a deployed URL. |
| List your recent scans (id, URL, score, grade, status, date). |
| Fetch the full report for a scan id as Markdown. |
Install
You need a CanaryUsers token: canaryusers.ai/dashboard → CI & API → toggle MCP on → copy your token.
Generic (Cursor ~/.cursor/mcp.json, Windsurf, VS Code, Claude Desktop):
{
"mcpServers": {
"canaryusers": {
"url": "https://www.canaryusers.ai/api/mcp",
"headers": {
"Authorization": "Bearer YOUR_CANARYUSERS_TOKEN"
}
}
}
}Claude Code (CLI):
claude mcp add --transport http canaryusers https://www.canaryusers.ai/api/mcp \
--header "Authorization: Bearer YOUR_CANARYUSERS_TOKEN"Then just ask your assistant: "Scan my app at https://… with CanaryUsers."
Pricing
Quick scans are free. Deep scans use credits (the credit model is roughly 1 credit = 1 scanned page). Plans and monthly allotments change over time, so this repo intentionally does not hardcode the numbers — see the live pricing for current plans and limits:
➡️ https://www.canaryusers.ai/#pricing (single source of truth)
Links
Setup & token: https://www.canaryusers.ai/mcp
Pricing (live): https://www.canaryusers.ai/#pricing
Official MCP Registry: https://registry.modelcontextprotocol.io/v0/servers?search=canaryusers
License
MIT — applies to the contents of this metadata/docs repo. The CanaryUsers service itself is a separate hosted product.
This server cannot be deployed
Maintenance
Related MCP Connectors
AI QA tester — real browsers scan sites for bugs, SEO, perf, and accessibility issues via chat.
AI users run real tasks on your live site and show where they get stuck, with a replay of every step
Deploy AI user personas to validate user journeys at scale. Find UX friction before real users do.
Browser-based QA for AI-built software. Test pages with real browsers via agents.
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