playwright-network-chaos-mcp
Enables AI agents to simulate network failures and test application resilience in Playwright browser sessions.
README
playwright-network-chaos-mcp 🐸💥
An MCP server that gives AI agents dynamic network chaos control over Playwright browser sessions.
Your tests run on perfect networks. Your users don't. This MCP lets AI agents simulate API outages, inject latency, drop connections mid-flight, and block third-party resources — then assert whether the app handles it gracefully.
🤔 The Problem
CI environments have flawless connectivity. APIs respond in milliseconds. CDNs never go down. So your tests pass — and then production breaks when the payment service returns a 503, the network drops mid-checkout, or Google Analytics hangs for 8 seconds and freezes the page.
AI agents writing Playwright tests have no way to introduce or reason about network instability. They can't ask:
- 🙈 Does the checkout page show an error state when the payment API fails?
- 🙈 Does the skeleton loader appear while the dashboard API is slow?
- 🙈 Does the app still work if all tracking scripts are blocked?
- 🙈 What happens if the network drops after the order is submitted but before the response arrives?
playwright-network-chaos-mcp fixes that.
🛠️ Tools
simulate_api_failure
Intercepts requests matching a pattern and forces them to return an error status code. Checks if the app shows a fallback UI.
{
"url": "https://your-app.com/checkout",
"intercept_pattern": "**/api/payment**",
"status_code": 503,
"fallback_selector": ".error-boundary",
"wait_ms": 2000
}
{
"intercepted_count": 2,
"fallback_found": true,
"fallback_selector": ".error-boundary",
"page_state": {
"page_errors": [],
"console_errors": ["Failed to load resource: 503"]
}
}
inject_latency
Adds artificial delay to matching requests. Checks if loading states appear while the app waits.
{
"url": "https://your-app.com/dashboard",
"intercept_pattern": "**/api/**",
"latency_ms": 3000,
"jitter_ms": 500,
"loading_selector": ".skeleton-loader"
}
{
"intercepted_count": 4,
"intercepted_requests": [
{ "url": "https://api.your-app.com/users", "method": "GET", "delay_ms": 3241 }
],
"loading_state_found": true,
"load_time_ms": 3890
}
block_resources
Aborts requests to specified URL patterns — for testing third-party outages (analytics, CDNs, tracking pixels).
{
"url": "https://your-app.com",
"block_patterns": ["**/analytics**", "*.doubleclick.net/**", "**/hotjar**"],
"core_content_selector": ".main-content",
"wait_ms": 2000
}
{
"blocked_count": 7,
"blocked_urls": ["https://www.google-analytics.com/analytics.js", "..."],
"core_content_found": true,
"page_state": { "page_errors": [], "console_errors": [] }
}
simulate_network_drop
Aborts requests mid-flight after a delay — simulating connection loss between request and response.
{
"url": "https://your-app.com/checkout",
"intercept_pattern": "**/api/order**",
"drop_after_ms": 800,
"fallback_selector": ".network-error-toast",
"wait_ms": 3000
}
{
"intercepted_count": 1,
"fallback_found": true,
"fallback_selector": ".network-error-toast",
"page_state": { "page_errors": ["TypeError: Failed to fetch"] }
}
trigger_system_network_error
Aborts requests with an OS-level error code — simulating DNS failures, firewall blocks, and connection resets.
{
"url": "https://your-app.com/dashboard",
"intercept_pattern": "**/api/**",
"error_code": "addressunreachable",
"fallback_selector": ".network-error"
}
{
"error_code": "addressunreachable",
"intercepted_count": 3,
"fallback_found": true,
"page_state": { "page_errors": [], "console_errors": ["net::ERR_ADDRESS_UNREACHABLE"] }
}
simulate_stateful_failure
Fails the first N requests then lets subsequent ones succeed — testing retry logic and recovery flows.
{
"url": "https://your-app.com/dashboard",
"intercept_pattern": "**/api/data**",
"http_status": 503,
"failure_count": 2,
"success_payload": "{\"data\":[]}",
"fallback_selector": ".retry-button"
}
{
"failure_count": 2,
"actual_failed": 2,
"actual_succeeded": 1,
"intercepted_requests": [
{ "url": "...", "method": "GET", "status": 503, "attempt": 1, "outcome": "failed" },
{ "url": "...", "method": "GET", "status": 200, "attempt": 3, "outcome": "passed" }
],
"fallback_found": true
}
inject_response_corruption
Serves malformed responses at the protocol level — unterminated JSON, content-length lies, or truncated payloads.
{
"url": "https://your-app.com/checkout",
"intercept_pattern": "**/api/order**",
"corruption_type": "malformed_json",
"fallback_selector": ".parse-error"
}
{
"corruption_type": "malformed_json",
"intercepted_count": 1,
"fallback_found": false,
"page_state": { "page_errors": ["SyntaxError: Unexpected token u in JSON"] }
}
assert_chaos_handled
Injects a chaos HTTP status and returns a structured pass/fail verdict — chaos_survived is true only when the fallback UI appears and there are no unhandled JS exceptions.
{
"url": "https://your-app.com/checkout",
"intercept_pattern": "**/api/**",
"http_status": 500,
"expected_fallback_selector": ".error-boundary"
}
{
"http_status": 500,
"unhandled_exceptions": [],
"console_errors": ["Failed to load resource: 500"],
"fallback_ui_detected": true,
"chaos_survived": true
}
🚀 Installation
npx playwright-network-chaos-mcp
Or install globally:
npm install -g playwright-network-chaos-mcp
npx playwright install chromium
Claude Desktop config
{
"mcpServers": {
"playwright-network-chaos-mcp": {
"command": "npx",
"args": ["-y", "playwright-network-chaos-mcp"]
}
}
}
💡 Example Agent Prompts
"Check if the checkout page shows a proper error state when the payment API returns 503"
"Simulate a 3 second API delay on the dashboard and verify the skeleton loader appears"
"Block all analytics and tracking scripts and confirm the main content still loads"
"Drop the order submission request mid-flight and check if the user sees an error message"
"Simulate DNS failure for the API and check if the error boundary renders"
"Fail the first 3 requests then succeed — does the app retry and recover automatically?"
"Inject malformed JSON and assert the app doesn't crash — return a chaos verdict"
🔗 Related Projects
- playwright-trace-decoder-mcp — root-cause analysis of CI failures from Playwright traces
- flakiness-knowledge-graph-mcp — knowledge graph of flaky test patterns
- ast-impact-mapper-mcp — find affected tests from code changes via TypeScript AST
- zod-contract-mock-forge-mcp — deterministic mock generation from Zod schemas
- playwright-spatial-layout-mcp — geometric spatial awareness of web layouts
📄 License
MIT © vola-trebla
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。