Playwrightess

The Playwrightess MCP server gives your AI agents a persistent Playwright environment.

Forget restarting browser sessions between commands. It maintains state through a single JavaScript interface called playwright_eval. You write standard Playwright scripts, but the context sticks around.

Need to fill forms across multiple pages? Handle authentication flows? Scrape dynamic sites without reloading? This handles it. It’s built for agents that need continuity, not one-off interactions.

Features

  • ✨ Persistent browser context between API calls
  • 🤖 Single playwright_eval entry point for all Playwright operations
  • ⚡ JavaScript-first interface (no JSON parsing headaches)

Use Cases

  • Auth workflows that don’t break: Agents log into apps once, then navigate protected routes across calls. No cookie-juggling or session resets. Regular Playwright MCP restarts the browser each time; this keeps it warm.
  • Multi-step form filling: Booking flights, filing reports, tasks requiring 5+ page hops. State persistence means your agent doesn’t lose progress when switching contexts.
  • Dynamic site scraping: Sites that load data via XHR after login? Maintain the session, wait for elements, and extract data across requests without re-authing.
  • Debugging agent hallucinations: When your agent claims “the button exists but won’t click,” inspect the actual DOM state from the previous step. No more guessing.

How To Use It

1. Clone the MCP server and install it:

npm install
npm run build

2. Configure your MCP client (e.g., in mcp.json):

{  
  "mcpServers": {  
    "playwriter-mcp": {  
      "type": "stdio",  
      "command": "node",  
      "args": ["/your/path/to/dist/index.js"],  
      "env": {}  
    }  
  }  
}  

3. Now call playwright_eval with raw Playwright code. Example:

// First call: Log in and store session  
await playwright_eval(`
const { chromium } = require('playwright');
const browser = await chromium.launch();
const page = await browser.newPage();
await page.goto('https://app.example/login');
await page.fill('#email', '[email protected]');
await page.fill('#password', 'secret');
await page.click('#submit');
// Cookies persist for next call
`);

// Second call: Use the same session
await playwright_eval(`
// No login needed—cookies still valid
await page.goto('https://app.example/dashboard');
console.log(await page.textContent('.welcome'));
`);

FAQs

Q: Why not just use regular Playwright MCP?
A: Standard Playwright MCP restarts the browser for every request. Playwrightess keeps the session alive, critical for workflows needing continuity. If your agent handles multi-page tasks, this cuts 70% of your error handling.

Q: Memory leaks? How long can sessions last?
A: Test it yourself. I ran sessions for 2 hours scraping e-commerce sites. But it’s experimental. Don’t trust it for days-long runs. Monitor memory; restart the MCP server if usage spikes.

Q: Can I use this with non-JS agents like Python?
A: Only if your agent speaks JavaScript. The playwright_eval interface demands valid JS strings. Wrap calls in your agent’s tool executor, but the payload must be JS.

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FAQs

Q: What exactly is the Model Context Protocol (MCP)?

A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.

Q: How is MCP different from OpenAI's function calling or plugins?

A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.

Q: Can I use MCP with frameworks like LangChain?

A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.

Q: Why was MCP created? What problem does it solve?

A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.

Q: Is MCP secure? What are the main risks?

A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.

Q: Who is behind MCP?

A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.

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