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    Mcp Aoai Web Browsing

    A minimal Model Context Protocol 🖥️ server/client🧑‍💻with Azure OpenAI and 🌐 web browser control via Playwright.

    30 stars
    Python
    Updated Apr 7, 2025
    azure-openai
    mcp
    mcp-client
    mcp-server
    model-context-protocol
    playwright
    web-automation

    Documentation

    MCP Server & Client implementation for using Azure OpenAI

    • A minimal server/client application implementation utilizing the Model Context Protocol (MCP) and Azure OpenAI.

    1. The MCP server is built with FastMCP.

    2. Playwright is an an open source, end to end testing framework by Microsoft for testing your modern web applications.

    3. The MCP response about tools will be converted to the OpenAI function calling format.

    4. The bridge that converts the MCP server response to the OpenAI function calling format customises the MCP-LLM Bridge implementation.

    5. To ensure a stable connection, the server object is passed directly into the bridge.

    Model Context Protocol (MCP)

    Model Context Protocol (MCP) MCP (Model Context Protocol) is an open protocol that enables secure, controlled interactions between AI applications and local or remote resources.

    Official Repositories

    • MCP Python SDK
    • Create Python Server
    • MCP Servers

    Community Resources

    • Awesome MCP Servers
    • MCP on Reddit

    Related Projects

    • FastMCP: The fast, Pythonic way to build MCP servers.
    • Chat MCP: MCP client
    • MCP-LLM Bridge: MCP implementation that enables communication between MCP servers and OpenAI-compatible LLMs

    MCP Playwright

    • MCP Playwright server
    • Microsoft Playwright for Python

    Configuration

    During the development phase in December 2024, the Python project should be initiated with 'uv'. Other dependency management libraries, such as 'pip' and 'poetry', are not yet fully supported by the MCP CLI.

    1. Rename .env.template to .env, then fill in the values in .env for Azure OpenAI:

    bash
    AZURE_OPEN_AI_ENDPOINT=
        AZURE_OPEN_AI_API_KEY=
        AZURE_OPEN_AI_DEPLOYMENT_MODEL=
        AZURE_OPEN_AI_API_VERSION=

    1. Install uv for python library management

    bash
    pip install uv
        uv sync

    1. Execute python chatgui.py

    • The sample screen shows the client launching a browser to navigate to the URL.

    w.r.t. 'stdio'

    stdio is a transport layer (raw data flow), while JSON-RPC is an application protocol (structured communication). They are distinct but often used interchangeably, e.g., "JSON-RPC over stdio" in protocols.

    Tool description

    cmd
    @self.mcp.tool()
    async def playwright_navigate(url: str, timeout=30000, wait_until="load"):
        """Navigate to a URL.""" -> This comment provides a description, which may be used in a mechanism similar to function calling in LLMs.
    
    # Output
    Tool(name='playwright_navigate', description='Navigate to a URL.', inputSchema={'properties': {'url': {'title': 'Url', 'type': 'string'}, 'timeout': {'default': 30000, 'title': 'timeout', 'type': 'string'}

    Tip: uv

    • features
    code
    uv run: Run a script.
    uv venv: Create a new virtual environment. By default, '.venv'.
    uv add: Add a dependency to a script
    uv remove: Remove a dependency from a script
    uv sync: Sync (Install) the project's dependencies with the environment.

    Tip

    • taskkill command for python.exe
    cmd
    taskkill /IM python.exe /F
    • Visual Code: Python Debugger: Debugging with launch.json will start the debugger using the configuration from .vscode/launch.json.

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