chore: import upstream snapshot with attribution

python/samples/concepts/mcp/mcp_as_plugin.py

@@ -0,0 +1,122 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+import os
+
+from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
+from semantic_kernel.connectors.mcp import MCPStdioPlugin
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.utils.logging import setup_logging
+
+"""
+This sample demonstrates how to build a conversational chatbot
+using Semantic Kernel,
+it creates a Plugin from a MCP server config and adds it to the kernel.
+The chatbot is designed to interact with the user, call MCP tools
+as needed, and return responses.
+
+To run this sample, make sure to run:
+`pip install semantic-kernel[mcp]`
+
+or install the mcp package manually.
+
+In addition, different MCP Stdio servers need different commands to run.
+For example, the Github plugin requires `npx`, others use `uvx` or `docker`.
+
+Make sure those are available in your PATH.
+"""
+
+# System message defining the behavior and persona of the chat bot.
+system_message = """
+You are a chat bot. And you help users interact with Github.
+You are especially good at answering questions about the Microsoft semantic-kernel project.
+You can call functions to get the information you need.
+"""
+
+setup_logging()
+logging.getLogger("semantic_kernel.connectors.mcp").setLevel(logging.DEBUG)
+
+# Create and configure the kernel.
+kernel = Kernel()
+
+# You can select from the following chat completion services that support function calling:
+# - Services.OPENAI
+# - Services.AZURE_OPENAI
+# - Services.AZURE_AI_INFERENCE
+# - Services.ANTHROPIC
+# - Services.BEDROCK
+# - Services.GOOGLE_AI
+# - Services.MISTRAL_AI
+# - Services.OLLAMA
+# - Services.ONNX
+# - Services.VERTEX_AI
+# - Services.DEEPSEEK
+# Please make sure you have configured your environment correctly for the selected chat completion service.
+chat_service, settings = get_chat_completion_service_and_request_settings(Services.OPENAI)
+
+# Configure the function choice behavior. Here, we set it to Auto, where auto_invoke=True by default.
+# With `auto_invoke=True`, the model will automatically choose and call functions as needed.
+settings.function_choice_behavior = FunctionChoiceBehavior.Auto()
+
+kernel.add_service(chat_service)
+
+# Create a chat history to store the system message, initial messages, and the conversation.
+history = ChatHistory()
+history.add_system_message(system_message)
+
+
+async def chat() -> bool:
+    """
+    Continuously prompt the user for input and show the assistant's response.
+    Type 'exit' to exit.
+    """
+    try:
+        user_input = input("User:> ")
+    except (KeyboardInterrupt, EOFError):
+        print("\n\nExiting chat...")
+        return False
+    if user_input.lower().strip() == "exit":
+        print("\n\nExiting chat...")
+        return False
+
+    history.add_user_message(user_input)
+    result = await chat_service.get_chat_message_content(history, settings, kernel=kernel)
+    if result:
+        print(f"Mosscap:> {result}")
+        history.add_message(result)
+
+    return True
+
+
+async def main() -> None:
+    # Create a plugin from the MCP server config and add it to the kernel.
+    # The MCP server plugin is defined using the MCPStdioPlugin class.
+    # The command and args are specific to the MCP server you want to run.
+    # For example, the Github MCP Server uses `npx` to run the server.
+    # There are also MCPSsePlugin and MCPStreamableHttpPlugin, which take a URL.
+    async with MCPStdioPlugin(
+        name="Github",
+        description="Github Plugin",
+        command="docker",
+        args=["run", "-i", "--rm", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN", "ghcr.io/github/github-mcp-server"],
+        env={"GITHUB_PERSONAL_ACCESS_TOKEN": os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN")},
+    ) as github_plugin:
+        # instead of using this async context manager, you can also use:
+        # await github_plugin.connect()
+        # and then await github_plugin.close() at the end of the program.
+
+        # Add the plugin to the kernel.
+        kernel.add_plugin(github_plugin)
+
+        # Start the chat loop.
+        print("Welcome to the chat bot!\n  Type 'exit' to exit.\n")
+        chatting = True
+        while chatting:
+            chatting = await chat()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())