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VibeShift Web tester

@Ilikepizza2

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About VibeShift Web tester

[MCP Server] The Security Agent for AI assisted coding

#testing#debugging#qa#web#mcp#debugging-tool#playwright#mcp-server#vibe-coding#vibe-testing

Basic information

Category

Other

License

Apache-2.0

Runtime

python

Transports

stdio

Publisher

Ilikepizza2

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "VibeShift": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Tools

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Overview

What is VibeShift Web tester?

VibeShift Web tester is an MCP server that acts as an automated security engineer for AI coding assistants. It analyzes AI-generated code for vulnerabilities, enables AI-driven remediation, and provides automated browser testing capabilities, including test recording, execution, and discovery.

How to use VibeShift Web tester?

Install the repository, set up a Python virtual environment, install dependencies and Playwright browsers, configure an LLM API key in .env, and add the MCP server configuration to your AI coding assistant. Interact via natural language commands such as “Scan this code for security issues” or “Record a test for login.”

Key features of VibeShift Web tester

  • MCP integration with Cursor, Copilot, Roo Code and others
  • Automated static (Semgrep) and dynamic (Nuclei) security scanning
  • AI-assisted test recording via natural language descriptions
  • Deterministic test execution with Playwright
  • AI-powered test discovery by crawling websites
  • Regression testing and self-healing of existing tests

Use cases of VibeShift Web tester

  • Automated security review of AI-generated code before commit
  • Recording and running browser tests for web applications
  • Discovering and suggesting test steps for websites via crawling
  • Regression testing to catch bugs after code changes

FAQ from VibeShift Web tester

What does VibeShift Web tester do that security scanners alone don’t?

It integrates directly into AI coding workflows, triggering on code generation and returning vulnerability reports that the AI assistant can use to suggest or apply fixes automatically.

What are the runtime requirements?

Python 3.10+, an LLM API key (Gemini 2.0 Flash recommended), and Playwright browsers installed via patchright install --with-deps.

Where are test results and recordings stored?

Recorded tests, execution results, and discovery outputs are saved as JSON files in the output/ directory.

How does authentication work?

No external authentication is needed; configure an LLM API key in the .env file (e.g., LLM_API_KEY="your_key").

Which transports does the server use?

Communication occurs via the Model Context Protocol (MCP), typically over stdin/stdout or a socket, as specified in the MCP configuration.

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