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MCP-AOAI-Web-Browsing
Ein minimales Modellkontextprotokoll 🖥️ Server/Client 🧑💻With Azure OpenAI und 🌐 Webbrowsersteuerung über Dramatiker.
3 years
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MCP Server & Client implementation for using Azure OpenAI
-
A minimal server/client application implementation utilizing the Model Context Protocol (MCP) and Azure OpenAI.
- The MCP server is built with
FastMCP
. -
Playwright
is an an open source, end to end testing framework by Microsoft for testing your modern web applications. - The MCP response about tools will be converted to the OpenAI function calling format.
- The bridge that converts the MCP server response to the OpenAI function calling format customises the
MCP-LLM Bridge
implementation. - To ensure a stable connection, the server object is passed directly into the bridge.
- The MCP server is built with
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 MCP 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=
-
Install
uv
for python library managementpip install uv uv sync
-
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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Reviews

user_njhpFuWb
I'm thoroughly impressed with the MCP Ping-Pong Server with FastAPI by kimtth! This intuitive and well-structured server provides an excellent example of how FastAPI can be utilized efficiently. It's responsive and easy to set up with clear documentation. A perfect solution for developers looking to implement ping-pong mechanics in their applications. Highly recommended! Check it out here: https://mcp.so/server/mcp-fastapi-ping-pong/kimtth