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 con

... View full README on GitHub