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. The client is created by ChatGPT to call the MCP server.
3. The server will call a function on the server, which is created using Playwright (Web Testing and Automation framework created by MS).
4. The MCP response about tools will be converted to the OpenAI function calling format.
5. The bridge that converts the MCP server response to the OpenAI function calling format customises the implementation from the MCP-LLM Bridge. 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

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

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 CLI.

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=

Execute chatgui.py
- The sample screen shows the client launching a browser to navigate to the URL.
<img alt="chatgui" src="doc/chatgui_gpt_generate.png" width="300"/>

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'}

uv

features

pip install uv

uv run: Run a script.
uv venv: Create a new virtual environment. By default, '.venv'.
uv add --script: Add a dependency to a script
uv remove --script: 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.