Vibetest Use

Vibetest Use is an MCP server that enables multiple Browser-Use agents to go through your site looking for UI bugs, broken links, accessibility problems, and other technical hiccups.

Features

  • 🤖 Multi-Agent Testing: Deploys several browser agents to test your site.
  • 🌐 Live & Localhost Support: Works with public URLs and local development servers (e.g., localhost:3000).
  • 🐞 UI Bug Detection: Helps spot visual inconsistencies and functional errors in the user interface.
  • 🔗 Broken Link Checker: Scans for and reports non-functional links.
  • Accessibility Insights: Identifies common accessibility issues.
  • ⚙️ Configurable Agent Count: You can specify the number of agents (default is 3) – more agents can mean more thorough testing.
  • 👻 Headless Mode: Option to run tests without a visible browser window, which is great for CI/CD pipelines or server-based testing.

How To Use It

1. Install dependencies:

# Create and activate a virtual environment
uv venv
source .venv/bin/activate
# Install Vibetest and its dependencies
uv pip install -e .

2. Connect it to your MCP clients

Claude Code

# Add the Vibetest MCP server
claude mcp add vibetest /full/path/to/vibetest-use/.venv/bin/vibetest-mcp -e GOOGLE_API_KEY="YOUR_API_KEY_HERE"

Remember to replace /full/path/to/vibetest-use/ with the actual path to where you installed Vibetest Use, and YOUR_API_KEY_HERE with your actual Google API key.

After that, you can check if it’s connected within Claude:

> claude
> /mcp
  ⎿  MCP Server Status
     • vibetest: connected

Cursor

  1. Open Cursor Settings.
  2. Click on “MCP” in the sidebar.
  3. Click “Add Server” (or the “+” button).
  4. You’ll need to manually edit the configuration JSON. It should look something like this:
{
  "mcpServers": {
    "vibetest": {
      "command": "/full/path/to/vibetest-use/.venv/bin/vibetest-mcp",
      "env": {
        "GOOGLE_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

3. Once it’s connected, you can tell it what to do:

> Vibetest my website with 5 agents: browser-use.com
> Run vibetest on localhost:3000
> Run a headless vibetest on localhost:8080 with 10 agents

4. Available parameters

  • URL: The website you want to test. This can be anything like https://example.com, localhost:3000, or http://dev.mysite.com.
  • Number of agents: You can specify how many agents to use, like 5 agents or 2 agents. The default is 3. More agents generally means it can cover more ground or do it faster.
  • Headless mode: By default, it runs non-headless (you see the browser windows). You can switch to headless if you prefer it to run in the background.

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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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