Skeleton MCP Server
A template project for building Model Context Protocol servers with FastMCP framework, providing example CRUD API implementations, Docker support, and development best practices.
README
Playwright MCP Proxy
A proxy server for Microsoft's playwright-mcp that provides efficient handling of large binary data (screenshots, PDFs) through blob storage and supports browser pools for concurrent operations.
Version 2.0.0: Now with browser pools! Run multiple isolated browser instances with different configurations simultaneously.
Features
- Browser Pools: Multiple isolated browser instances organized into named pools with different configurations
- Concurrent Operations: Lease browser instances for exclusive use, enabling parallel browser automation
- Playwright Browser Automation: Full access to all playwright-mcp browser automation tools
- Stealth Mode: Built-in anti-detection capabilities (see STEALTH.md)
- Efficient Binary Handling: Large screenshots and PDFs automatically stored as blobs to reduce token usage
- Blob Storage: Built-in blob management using mcp-mapped-resource-lib
- Automatic Cleanup: TTL-based automatic expiration of old blobs
- Docker Support: Containerized deployment with multi-runtime support (Python + Node.js + Playwright)
- Health Monitoring: Real-time pool status and instance health checks
Quick Start
Prerequisites
- Python 3.10 or higher
- Node.js 18+ (for playwright-mcp)
- uv package manager (recommended)
- Docker (optional, for containerized deployment)
Installation
- Clone this repository:
git clone <this-repo> playwright-proxy-mcp
cd playwright-proxy-mcp
- Install dependencies:
uv sync
- Create your environment file:
cp .env.example.single-pool .env
# Edit .env with your configuration
- Run the server:
uv run playwright-proxy-mcp
The server will:
- Start the playwright-mcp subprocess(es) via npx
- Initialize blob storage
- Initialize browser pools
- Listen for MCP client connections on stdio
Browser Pools
Overview
Browser pools allow you to run multiple browser instances with different configurations:
# Global defaults (apply to all pools)
PW_MCP_PROXY_BROWSER=chromium
PW_MCP_PROXY_HEADLESS=true
# Define a pool with 3 instances
PW_MCP_PROXY__DEFAULT_INSTANCES=3
PW_MCP_PROXY__DEFAULT_IS_DEFAULT=true
PW_MCP_PROXY__DEFAULT_DESCRIPTION="General purpose browsing"
# Instance-level overrides
PW_MCP_PROXY__DEFAULT__0_BROWSER=firefox # Instance 0 uses Firefox
PW_MCP_PROXY__DEFAULT__1_ALIAS=debug # Instance 1 has alias "debug"
PW_MCP_PROXY__DEFAULT__1_HEADLESS=false # Instance 1 runs headed
Using Pools
All browser tools accept optional browser_pool and browser_instance parameters:
# Use default pool, FIFO instance selection
await browser_navigate(url="https://example.com")
# Use specific pool
await browser_navigate(url="https://example.com", browser_pool="FIREFOX")
# Use specific instance by alias
await browser_navigate(url="https://example.com", browser_instance="debug")
Monitoring Pools
# Get status of all pools
status = await browser_pool_status()
for pool in status["pools"]:
print(f"{pool['name']}: {pool['available_instances']}/{pool['total_instances']} available")
See docs/BROWSER_POOLS_SPEC.md for complete configuration reference.
Docker Deployment
Build and run with Docker Compose:
docker compose up -d
This will:
- Build a container with Python, Node.js, and Playwright browsers
- Create persistent volumes for blob storage and playwright output
- Start the proxy server
Configuration
Configure the proxy via environment variables in .env:
Global Browser Settings
PW_MCP_PROXY_BROWSER: Browser to use (chromium, firefox, webkit) - default: chromiumPW_MCP_PROXY_HEADLESS: Run headless - default: truePW_MCP_PROXY_CAPS: Capabilities (vision,pdf,testing,tracing) - default: vision,pdfPW_MCP_PROXY_TIMEOUT_ACTION: Action timeout in ms - default: 15000PW_MCP_PROXY_TIMEOUT_NAVIGATION: Navigation timeout in ms - default: 30000
Pool Configuration
PW_MCP_PROXY__<POOL>_INSTANCES: Number of instances in poolPW_MCP_PROXY__<POOL>_IS_DEFAULT: Mark as default poolPW_MCP_PROXY__<POOL>_DESCRIPTION: Pool descriptionPW_MCP_PROXY__<POOL>__<ID>_BROWSER: Browser for specific instancePW_MCP_PROXY__<POOL>__<ID>_ALIAS: Alias for specific instancePW_MCP_PROXY__<POOL>__<ID>_HEADLESS: Headless mode for specific instance
Stealth Settings (Anti-Detection)
PW_MCP_PROXY_STEALTH_MODE: Enable built-in stealth mode - default: falsePW_MCP_PROXY_USER_AGENT: Custom user agent string - optionalPW_MCP_PROXY_INIT_SCRIPT: Path to custom init script - optionalPW_MCP_PROXY_IGNORE_HTTPS_ERRORS: Ignore HTTPS errors - default: false
See docs/STEALTH.md for detailed stealth configuration.
Blob Storage Settings
BLOB_STORAGE_ROOT: Storage directory - default: /mnt/blob-storageBLOB_MAX_SIZE_MB: Max size per blob - default: 500BLOB_TTL_HOURS: Time-to-live for blobs - default: 24BLOB_SIZE_THRESHOLD_KB: Size threshold for blob storage - default: 50BLOB_CLEANUP_INTERVAL_MINUTES: Cleanup frequency - default: 60
See example env files in the repository root for complete configuration examples.
How It Works
Binary Data Interception
The proxy automatically detects large binary data in playwright tool responses:
- When playwright tools return screenshots or PDFs
- If the data size exceeds the threshold (default: 50KB)
- The proxy stores the binary data as a blob
- The response is transformed to include a blob reference instead
Before (direct playwright-mcp):
{
"screenshot": "data:image/png;base64,iVBORw0KGgo...500KB of data..."
}
After (through proxy):
{
"screenshot": "blob://1733577600-a3f2c1d9e4b5.png",
"screenshot_size_kb": 500,
"screenshot_mime_type": "image/png",
"screenshot_expires_at": "2024-12-08T10:00:00Z"
}
Retrieving Blobs
Blob retrieval is handled by a separate MCP Resource Server. See mcp-mapped-resource-lib for details.
Available Tools
Browser Tools
All playwright-mcp tools are available with browser pool support:
browser_navigate: Navigate to a URLbrowser_click: Click an elementbrowser_fill: Fill a form fieldbrowser_screenshot: Take a screenshot (auto-stored as blob if large)browser_snapshot: Get ARIA snapshotbrowser_evaluate: Execute JavaScript- And 40+ more tools...
All tools accept optional browser_pool and browser_instance parameters.
Pool Management
browser_pool_status(pool_name): Get pool health, lease activity, and instance status
Architecture
┌─────────────────────────────────┐
│ MCP Client (Claude Desktop) │
└────────────┬────────────────────┘
│ stdio
┌────────────▼────────────────────┐
│ FastMCP Proxy (Python) │
│ - Pool Manager │
│ - Binary Interception │
│ - Blob Storage Integration │
│ - Instance Leasing (FIFO) │
└────────────┬────────────────────┘
│ stdio (per instance)
┌────────────▼────────────────────┐
│ playwright-mcp instances │
│ - Browser Automation │
│ - Screenshot/PDF Generation │
└─────────────────────────────────┘
Testing
Run the test suite:
uv run pytest -v
Lint the code:
uv run ruff check src/ tests/
uv run ruff format src/ tests/
Project Structure
src/playwright_proxy_mcp/
├── server.py # Main MCP proxy server
├── types.py # TypedDict definitions
├── playwright/ # Playwright proxy components
│ ├── config.py # Configuration loading (pool config)
│ ├── pool_manager.py # Browser pool management
│ ├── process_manager.py # Subprocess management
│ ├── blob_manager.py # Blob storage wrapper
│ ├── middleware.py # Binary interception
│ └── proxy_client.py # Stdio transport integration
└── utils/
├── navigation_cache.py # TTL-based pagination cache
├── aria_processor.py # ARIA snapshot processing
└── jmespath_extensions.py # Custom JMESPath functions
Benefits
Token Savings
Large screenshots can consume 50,000+ tokens. With blob storage:
- Screenshots stored as blobs use ~100 tokens for the reference
- Retrieve full data only when needed
- Automatic cleanup prevents storage bloat
Concurrent Operations
Browser pools enable:
- Parallel browser automation
- Instance isolation for concurrent tasks
- Different browser configurations for different use cases
Performance
- Faster response times for tool calls
- Reduced context window usage
- Efficient deduplication of identical screenshots
- FIFO instance leasing for fair resource allocation
Troubleshooting
npx not found
Ensure Node.js is installed and npx is in your PATH:
node --version
npx --version
Playwright browser installation fails
Install browsers manually:
npx playwright@latest install chromium --with-deps
Blob storage permissions
Ensure the blob storage directory is writable:
chmod -R 755 /mnt/blob-storage
Pool not starting
Check the pool configuration in your .env file. Ensure:
- At least one pool has
IS_DEFAULT=true - Instance counts are valid (positive integers)
- No alias conflicts with numeric instance IDs
License
MIT
Contributing
Contributions welcome! Please open an issue or pull request.
Resources
推荐服务器
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。
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。
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。
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。
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。
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。
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。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
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。