Azure OpenAI

local-only server

The server can only run on the client’s local machine because it depends on local resources.

Integrations

  • Uses .env configuration for storing Azure OpenAI credentials and settings.

  • References to GitHub repositories for MCP-related projects and resources, including the official MCP Python SDK, server implementations, and community resources.

  • Integrates with Azure OpenAI to provide AI model capabilities. The server implements a bridge that converts MCP responses to the OpenAI function calling format.

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

Community Resources

  • 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

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:
    AZURE_OPEN_AI_ENDPOINT= AZURE_OPEN_AI_API_KEY= AZURE_OPEN_AI_DEPLOYMENT_MODEL= AZURE_OPEN_AI_API_VERSION=
  2. Install uv for python library management
    pip install uv uv sync
  3. 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

@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

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
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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security - not tested
A
license - permissive license
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quality - not tested

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

  1. Model Context Protocol (MCP)
    1. Official Repositories
    2. Community Resources
    3. Related Projects
    4. MCP Playwright
    5. Configuration
    6. w.r.t. 'stdio'
    7. Tool description
    8. Tip: uv
    9. Tip