MCP Virtual User

MCP Virtual User

Enables end-to-end testing of voice-enabled web applications by simulating a synthetic user with a virtual microphone, virtual speakers, a real browser, and a viewable display, allowing audio injection, speech capture/transcription, and browser automation.

Category
访问服务器

README

MCP Virtual User

A synthetic human in a Docker container. Has ears, a mouth, eyes, and hands.

What is this?

A fully self-contained environment that simulates a real user interacting with web apps via voice and browser. It has:

  • Virtual microphone — MCP can inject audio (TTS or raw) that any app thinks is coming from a hardware mic
  • Virtual speakers — MCP can capture and transcribe whatever audio the OS plays back
  • Real browser — Chromium with Playwright control, persistent login sessions
  • Real display — Xvfb + VNC for debugging (watch what's happening live)
  • MCP interface — Everything exposed as tools via Streamable HTTP

Use Cases

  • Test ChatGPT voice mode — inject "What's the weather?" into the mic, capture ChatGPT's spoken response, transcribe it, assert on it
  • Test Gemini Live — same flow against Google's voice AI
  • Test our Mobile Mesh UI — full end-to-end voice conversation testing against our own app
  • Any voice-enabled web app — if it uses the browser's mic/speaker, we can test it

Architecture

┌─────────────────────────────────────────────────────────────┐
│  Docker Container                                            │
│                                                             │
│  ┌─────────────────────────────────────────────────────┐    │
│  │  MCP Server (port 8360)                              │    │
│  │                                                     │    │
│  │  Audio Tools:                                       │    │
│  │    tts_to_mic(text)     → Piper TTS → virtual mic   │    │
│  │    transcribe_speakers() → parec → Whisper STT      │    │
│  │    inject_audio(b64)    → raw audio → virtual mic   │    │
│  │    capture_audio(secs)  → raw audio from speakers   │    │
│  │    wait_for_speech()    → detect + transcribe       │    │
│  │                                                     │    │
│  │  Browser Tools:                                     │    │
│  │    browser_navigate, click, type, screenshot, etc.  │    │
│  │    browser_grant_mic_permission(origin)             │    │
│  │                                                     │    │
│  │  Screen Tools:                                      │    │
│  │    screen_screenshot, screen_size, vnc_url          │    │
│  └──────────┬──────────────────────────┬───────────────┘    │
│             │                          │                     │
│  ┌──────────▼──────────┐  ┌───────────▼───────────────┐    │
│  │  PulseAudio         │  │  Playwright + Chromium    │    │
│  │                     │  │                           │    │
│  │  virtual_mic ◀──────│──│── browser reads as mic    │    │
│  │  (pipe-source)      │  │                           │    │
│  │                     │  │  browser plays audio ──▶  │    │
│  │  virtual_speaker ───│──│── captured via .monitor   │    │
│  │  (null-sink)        │  │                           │    │
│  └─────────────────────┘  └───────────────────────────┘    │
│                                                             │
│  ┌─────────────────────┐  ┌───────────────────────────┐    │
│  │  Xvfb :99           │  │  x11vnc + noVNC          │    │
│  │  1920x1080x24       │  │  port 5900 / 6080        │    │
│  └─────────────────────┘  └───────────────────────────┘    │
└─────────────────────────────────────────────────────────────┘

Quick Start

# Build and start
docker compose up -d --build

# Watch the virtual display (open in your browser)
open http://localhost:6080/vnc.html?autoconnect=true

# Test the MCP server
curl http://localhost:8360/mcp -X POST \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"health","arguments":{}}}'

# Run smoke tests
bash test-smoke.sh

First-Time Setup (One-Time)

# 1. Build and start
docker compose up -d --build

# 2. Open VNC in your browser to see the desktop
open http://localhost:6080/vnc.html?autoconnect=true

# 3. In the VNC window, Chromium is running. Log into:
#    - https://chat.openai.com (ChatGPT)
#    - https://gemini.google.com (Gemini)

# 4. Export cookies to DragonsKeep so they persist
bash scripts/export-browser-cookies.sh

# 5. Done! Future container starts auto-inject the stored sessions.

Running Tests

# Infrastructure tests (no login required)
cd tests && pip install -r requirements.txt
pytest test_audio_roundtrip.py test_browser.py -v

# ChatGPT conversation tests (requires login)
pytest test_conversations.py -v -m chatgpt --timeout=120

# Gemini conversation tests
pytest test_conversations.py -v -m gemini --timeout=120

# Our Mesh UI conversation tests
pytest test_conversations.py -v -m meshui --timeout=120

# All conversation tests
pytest test_conversations.py -v --timeout=120

Example: Test ChatGPT Voice Mode

# From any MCP client (Kiro, our mesh agent, etc.)

# 1. Navigate to ChatGPT
browser_navigate("https://chat.openai.com")

# 2. Grant mic permission
browser_grant_mic_permission("https://chat.openai.com")

# 3. Click the voice mode button
browser_click("[data-testid='voice-mode-button']")

# 4. Speak a question (injected into the virtual mic)
tts_to_mic("What is the capital of France?")

# 5. Wait for ChatGPT to respond and transcribe what it says
response = wait_for_speech(timeout_seconds=15)
# response == "The capital of France is Paris."

# 6. Assert
assert "Paris" in response

Session Management

Login sessions persist in ./data/browser-profile/ (mounted volume).

For ChatGPT/Gemini auth tokens, use DragonsKeep:

  1. Store cookies in DragonsKeep as chatgpt-session / gemini-session
  2. On container start, inject them into the browser profile

Or: log in manually once via VNC (http://localhost:6080), session persists.

Ports

Port Service
8360 MCP Server (Streamable HTTP)
6080 noVNC (browser-based VNC viewer)
5900 VNC direct

MCP Tools

Audio

Tool Description
tts_to_mic Synthesize text → inject as microphone input
transcribe_speakers Capture speaker output → transcribe to text
inject_audio Push raw audio bytes into the virtual mic
capture_audio Record raw audio from the virtual speakers
wait_for_speech Detect speech on speakers, wait for it to finish, transcribe

Browser

Tool Description
browser_navigate Go to a URL
browser_click Click an element
browser_type Type into an input
browser_press_key Press a keyboard key
browser_screenshot Take a page screenshot
browser_get_text Get page text content
browser_evaluate Execute JavaScript
browser_wait_for_text Wait for text to appear
browser_url Get current URL
browser_grant_mic_permission Allow mic access for an origin

Screen

Tool Description
screen_screenshot Full desktop screenshot
screen_size Get display resolution
vnc_url Get the live VNC viewer URL

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选