UI Debugger

An MCP server that debugs UIs autonomously — so the AI that wrote your app can also test it, without a human clicking through every flow.

The problem

AI coding agents (Claude, etc.) are great at writing code. They're bad at knowing if the UI actually works. For backend code there are unit and integration tests. For UI, a human still has to open the app, log in, click around, and report what's broken. That human-in-the-loop is slow, boring, and the main bottleneck when an entire product is built by AI.

The idea

Eliminate the human from the UI-debug loop with an MCP server.

• A smart agent (Claude Code, Cursor, …) finishes a PR and wants to verify the UI.
• It hands a story to this server: "on web, log in and do X, Y, Z — tell me if it breaks."
• A small fast agent runs inside this server (via the Vercel AI SDK). It drives the browser or desktop, watches console + network, takes screenshots.
• It reports structured findings back: pass/fail, what broke, evidence.
• The smart agent fixes the code and asks again. Loop until the UI works.

Unlike playwright-mcp — where the smart model issues every single click itself — here the smart model stays high-level and delegates the whole clicking loop to the small agent.

How it's different from playwright-mcp

	playwright-mcp	UI Debugger MCP
Who clicks	smart model, one action per call	small agent, on its own
Tools exposed	many (click, type, snapshot…)	few (give a story, get findings)
Smart model cost	high (chatty)	low (high-level)
Output	raw page state	structured findings + evidence

Architecture — the three actors

Picture a boss, a fast blind driver, and a describer with eyes:

   ┌─────────────┐   MCP conversation    ┌──────────────────────────────────────┐
   │ smart agent │  start_debug ───────▶ │        UI Debugger MCP server         │
   │  (Claude)   │  send_message (live)  │                                       │
   │             │ ◀─────── get_findings │   ┌────────────┐     ┌────────────┐   │
   │ sets goals  │                       │   │  fast guy  │ look│ vision guy │   │
   │ fixes code  │                       │   │  (driver)  │────▶│  (eyes)    │   │
   │ loops       │                       │   │ deepseek   │◀────│  glm 5v    │   │
   └─────────────┘                       │   │ text·blind │ desc│ image      │   │
          ▲                              │   └─────┬──────┘     └────────────┘   │
          │ "works + looks nice"         │     observe / act (SQL-like)          │
          │ findings + screenshots       │         │ shared adapter contract     │
          └──────────────────────────────│─────────┼─────────────────────────────│
                                          └─────────┼─────────────────────────────┘
                                                    ▼
                              ┌──────────────┬──────────────┬──────────────┐
                              │  web (CDP)   │ desktop      │ android      │
                              │  browser     │ X11/Wayland  │ ADB          │
                              └──────────────┴──────────────┴──────────────┘

• smart agent — the boss (Claude/caller). Sends a goal, reads findings, fixes the code, loops. Stays high-level — never clicks.
• fast guy — the driver. Fast, cheap, text-only and blind. Runs the click loop on structure (DOM / a11y tree / view hierarchy). Default: deepseek.
• vision guy — the eyes. Multimodal. The driver calls look to ask "does this look right? is the button centred?" and gets a description back. Default: glm. Spent only when visual judgment is needed.

One goal: the UI works and looks nice. Full design in docs/idea/.

Every run keeps its screenshots and stitches them into a short captioned replay video — Claude attaches it to the PR so a reviewer sees the flow working in ~10 seconds (docs/idea/workspace.md).

Targets

One project can expose several debug targets. A large app can have all three:

Target	Protocol / how it's driven	Reads
web	CDP (Chrome DevTools Protocol), headless by default	DOM
desktop	X11 / Wayland input + AT-SPI	a11y tree / vision
mobile	ADB (uiautomator + screencap), Android	view hierarchy / vision

Three adapters, one shared contract. Each runs managed (server launches the target) or attach (connect to a running one via cdpUrl / adbSerial). Linux first. iOS is out of scope on Linux (macOS-only tooling).

Setup

Install like any local MCP server — one entry in your .mcp.json:

{
  "mcpServers": {
    "ui-debugger": {
      "command": "npx",
      "args": ["-y", "@developerz.ai/ui-debugger-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "OPENAI_BASE_URL": "https://openrouter.ai/api/v1"
      }
    }
  }
}

Then add a per-project .ui-debugger-mcp.json describing the app to debug (models, targets, urls). The fastest way is the init command:

npx @developerz.ai/ui-debugger-mcp init   # in your project root

ui-debugger-mcp init scaffolds a project for debugging (described in docs/idea/config.md):

• creates the workspace dir ./tmp/ui-debugger-mcp/
• writes a starter .ui-debugger-mcp.json (default deepseek/glm models, a web target stub) if one doesn't already exist
• adds tmp/ to .gitignore
• prints the .mcp.json snippet to paste (it never writes your API key)

Config files:

• .mcp.json → how to launch the server (command + secret key). Gitignored.
• .ui-debugger-mcp.json → how to debug this app (models, targets). Committed.

The server reads the current directory to pick the project session — open it in your repo and it debugs that repo.

Quickstart

# 1. Scaffold the project (run once in your app's root)
npx @developerz.ai/ui-debugger-mcp init

This creates ./tmp/ui-debugger-mcp/, writes a starter .ui-debugger-mcp.json, and prints the .mcp.json snippet to paste.

// 2. Paste into your project's .mcp.json (add your API key)
{
  "mcpServers": {
    "ui-debugger": {
      "command": "npx",
      "args": ["-y", "@developerz.ai/ui-debugger-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "OPENAI_BASE_URL": "https://openrouter.ai/api/v1"
      }
    }
  }
}

// 3. Edit .ui-debugger-mcp.json — set your app's URL
{
  "targets": {
    "web": { "adapter": "browser", "url": "http://localhost:3000" }
  }
}

// 4. In Claude Code (or any MCP client):
start_debug { target: "web", goal: "log in and add item 3 to the cart" }

// 5. Poll until done:
get_findings { session_id: "...", wait: true }

// 6. Read bugs[] + visual[] + summary. Fix code, repeat.

Using it

It's a conversation, not a remote control — five fat tools, not one-per-click:

Tool	What it does
start_debug	Open a run: { target, goal, criteria?, timeout? }. The small agent drives autonomously. Returns { session_id }.
get_findings	Poll status + structured findings (functional bugs + visual issues) + evidence. Long-poll with wait.
send_message	Talk to the running agent mid-flight — add work, redirect, or answer a question.
describe	List the configured targets + models for this project.
end_session	Close the run, free the browser/profile.

A run is always time-capped: start_debug's timeout (seconds) overrides the default 300s, so a session can never hang forever — it auto-ends and frees the profile lock when the cap fires.

Typical loop from a smart agent:

start_debug { target: "web", goal: "log in and add item 3 to the cart" }
→ poll get_findings (wait) until status is passed | failed
→ read bugs[] + visual[] + summary, fix the code, start_debug again

You can also drive it headless from a script with claude -p — see docs/claude/SKILL.md for the CLI recipe (MCP config, allowed tools, output formats).

CLI — check or stop a run

The ui-debugger-mcp binary doubles as a control CLI for the active run (reads state.json, no API key needed):

ui-debugger-mcp status   # which run is active, server pid, verdict, finding counts
ui-debugger-mcp stop     # gracefully end the run (frees the browser + profile)

Troubleshooting

Chrome not found The web adapter launches Chrome via the system PATH. Install Chrome/Chromium, or set executablePath in .ui-debugger-mcp.json:

"web": { "adapter": "browser", "url": "...", "executablePath": "/usr/bin/chromium-browser" }

Session locked — "another run is active" One Chrome profile = one run. If a previous run crashed without cleaning up:

npx @developerz.ai/ui-debugger-mcp stop   # graceful teardown

Or delete ./tmp/ui-debugger-mcp/state.json and restart the MCP server.

Run times out with no findings Default cap is 300 s. Raise it per-call:

start_debug { target: "web", goal: "...", timeout: 600 }

If the agent is stuck at login, add ?debug-ai=true to your app's login route (gated by ALLOW_AI_DEBUG_LOGIN) to skip captchas — see CLAUDE.md for the pattern.

get_findings returns empty bugs[] / visual[] The run may still be in progress — use wait: true:

get_findings { session_id: "...", wait: true }

Check ./tmp/ui-debugger-mcp/<project>/logs/agent.log for the agent's trace.

replay.mp4 not generated ffmpeg is optional. Install it and retry, or ignore — findings and screenshots still land without it.

Stack

• Bun + TypeScript (ships as npm, runs via npx/bunx)
• Vercel AI SDK — the agent loop (fast driver + vision describer)
• Any OpenAI-compatible router (OpenRouter default) — swap models per role. Defaults: deepseek (text) drives, glm (image) sees.
• CDP for web, X11/Wayland for desktop, ADB for Android
• stdio MCP transport

Status

All three adapters ship in v1:

Target	State
web	✅ shipped (CDP, headless + attach)
desktop	✅ shipped (X11/Wayland, AT-SPI + xdotool)
android	✅ shipped (ADB, uiautomator)

Replay video (replay.mp4, captioned stills → mp4 via ffmpeg) ships with the web adapter. ffmpeg is optional — absent gracefully, findings still land.

See docs/idea/ for design notes.

Docs

• docs/idea/overview.md — problem + idea
• docs/idea/architecture.md — system design
• docs/idea/adapters.md — adapter contract + targets
• docs/idea/desktop-control.md — Linux control tooling (X11/Wayland/mobile)
• docs/idea/agent-loop.md — the story → findings loop
• docs/idea/mcp-tools.md — two tool layers, SQL-like params, in-repo prompts
• docs/idea/models.md — the three actors (smart agent / fast guy / vision guy)
• docs/idea/config.md — config files
• docs/idea/workspace.md — per-project space + logs
• docs/claude/SKILL.md — driving claude as a headless CLI tool (generic)
• CLAUDE.md — instructions for AI agents working on this repo

Credits / influences

• ai-task-master — build template (orchestrator + subagents)
• gold-standards-in-ai — MCP & code conventions
• claude-code-bible — agent-first patterns
• Model Context Protocol