Python: Introducing support for using a MCP server as a plugin (#11334)

### Motivation and Context

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Adds MCP server configs and a function that turns the server into a
plugin, with each tools of the server represented as a function.
Adds a sample showing how to use that with a Github MCP server.

Closes: #10785 and #11190

With special thanks to @nmoeller 

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

---------

Co-authored-by: Nico Möller <nkm.moeller@gmail.com>

Author: eavanvalkenburg

python/pyproject.toml

@@ -79,6 +79,9 @@ hugging_face = [
     "sentence-transformers >= 2.2,< 5.0",
     "torch == 2.6.0"
 ]
+mcp = [
+    "mcp ~= 1.6"
+]
 mongo = [
     "pymongo >= 4.8.0, < 4.12",
     "motor >= 3.3.2,< 3.8.0"
@@ -114,7 +117,7 @@ qdrant = [
 redis = [
     "redis[hiredis] ~= 5.0",
     "types-redis ~= 4.6.0.20240425",
-    "redisvl >= 0.3.6",
+    "redisvl ~= 0.4"
 ]
 usearch = [
     "usearch ~= 2.16",

python/samples/concepts/mcp/README.md

@@ -0,0 +1,46 @@
+# Model Context Protocol
+
+The model context protocol is a standard created by Anthropic to allow models to share context with each other. See the [official documentation](https://modelcontextprotocol.io/introduction) for more information.
+
+It consists of clients and servers, and servers can be hosted locally, or they can be exposed as a online API.
+
+Our goal is that Semantic Kernel can act as both a client and a server.
+
+In this folder the client side of things is demonstrated. It takes the definition of a server and uses that to create a Semantic Kernel plugin, this plugin exposes the tools and prompts of the server as functions in the kernel.
+
+Those can then be used with function calling in a chat or agent.
+
+## Server types
+
+There are two types of servers, Stdio and Sse based. The sample shows how to use the Stdio based server, which get's run locally, in this case by using [npx](https://docs.npmjs.com/cli/v8/commands/npx).
+
+Some other common runners are [uvx](https://docs.astral.sh/uv/guides/tools/), for python servers and [docker](https://www.docker.com/), for containerized servers.
+
+The code shown works the same for a Sse server, only then a MCPSsePlugin needs to be used instead of the MCPStdioPlugin.
+
+The reverse, using Semantic Kernel as a server, is not yet implemented, but will be in the future.
+
+## Running the sample
+
+1. Make sure you have the [Node.js](https://nodejs.org/en/download/) installed.
+2. Make sure you have the [npx](https://docs.npmjs.com/cli/v8/commands/npx) available in PATH.
+3. The Github MCP Server uses a Github Personal Access Token (PAT) to authenticate, see [the documentation](https://github.com/modelcontextprotocol/servers/tree/main/src/github) on how to create one.
+4. Install Semantic Kernel with the mcp extra:
+
+```bash
+pip install semantic-kernel[mcp]
+```
+
+5. Run the sample:
+
+```bash
+cd python/samples/concepts/mcp
+python mcp_as_plugin.py
+```
+
+or:
+
+```bash
+cd python/samples/concepts/mcp
+python agent_with_mcp_plugin.py
+```

python/samples/concepts/mcp/agent_with_mcp_plugin.py

@@ -0,0 +1,119 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+from semantic_kernel.connectors.mcp import MCPStdioPlugin
+
+"""
+The following sample demonstrates how to create a chat completion agent that
+answers questions about Github using a Semantic Kernel Plugin from a MCP server. 
+The Chat Completion Service is passed directly via the ChatCompletionAgent constructor.
+Additionally, the plugin is supplied via the constructor.
+"""
+
+
+# Simulate a conversation with the agent
+USER_INPUTS = [
+    "What are the latest 5 python issues in Microsoft/semantic-kernel?",
+    "Are there any untriaged python issues?",
+    "What is the status of issue #10785?",
+]
+
+
+async def main():
+    # 1. Create the agent
+    async with MCPStdioPlugin(
+        name="Github",
+        description="Github Plugin",
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-github"],
+    ) as github_plugin:
+        agent = ChatCompletionAgent(
+            service=AzureChatCompletion(),
+            name="IssueAgent",
+            instructions="Answer questions about the Microsoft semantic-kernel github project.",
+            plugins=[github_plugin],
+        )
+
+        for user_input in USER_INPUTS:
+            # 2. Create a thread to hold the conversation
+            # If no thread is provided, a new thread will be
+            # created and returned with the initial response
+            thread: ChatHistoryAgentThread = None
+
+            print(f"# User: {user_input}")
+            # 3. Invoke the agent for a response
+            response = await agent.get_response(messages=user_input, thread=thread)
+            print(f"# {response.name}: {response} ")
+            thread = response.thread
+
+            # 4. Cleanup: Clear the thread
+            await thread.delete() if thread else None
+
+        """
+        Sample output:
+GitHub MCP Server running on stdio
+# User: What are the latest 5 python issues in Microsoft/semantic-kernel?
+# IssueAgent: Here are the latest 5 Python issues in the 
+[Microsoft/semantic-kernel](https://github.com/microsoft/semantic-kernel) repository:
+
+1. **[Issue #11358](https://github.com/microsoft/semantic-kernel/pull/11358)**  
+   **Title:** Python: Bump Python version to 1.27.0 for a release.  
+   **Created by:** [moonbox3](https://github.com/moonbox3)  
+   **Created at:** April 3, 2025  
+   **State:** Open  
+   **Comments:** 1  
+   **Description:** Bump Python version to 1.27.0 for a release.
+
+2. **[Issue #11357](https://github.com/microsoft/semantic-kernel/pull/11357)**  
+   **Title:** .Net: Version 1.45.0  
+   **Created by:** [markwallace-microsoft](https://github.com/markwallace-microsoft)  
+   **Created at:** April 3, 2025  
+   **State:** Open  
+   **Comments:** 0  
+   **Description:** Version bump for release 1.45.0.
+
+3. **[Issue #11356](https://github.com/microsoft/semantic-kernel/pull/11356)**  
+   **Title:** .Net: Fix bug in sqlite filter logic  
+   **Created by:** [westey-m](https://github.com/westey-m)  
+   **Created at:** April 3, 2025  
+   **State:** Open  
+   **Comments:** 0  
+   **Description:** Fix bug in sqlite filter logic.
+
+4. **[Issue #11355](https://github.com/microsoft/semantic-kernel/issues/11355)**  
+   **Title:** .Net: [MEVD] Validate that the collection generic key parameter corresponds to the model  
+   **Created by:** [roji](https://github.com/roji)  
+   **Created at:** April 3, 2025  
+   **State:** Open  
+   **Comments:** 0  
+   **Description:** We currently have validation for the TKey generic type parameter passed to the collection type, 
+   and we have validation for the key property type on the model.
+
+5. **[Issue #11354](https://github.com/microsoft/semantic-kernel/issues/11354)**  
+   **Title:** .Net: How to add custom JsonSerializer on a builder level  
+   **Created by:** [PawelStadnicki](https://github.com/PawelStadnicki)  
+   **Created at:** April 3, 2025  
+   **State:** Open  
+   **Comments:** 0  
+   **Description:** Inquiry about adding a custom JsonSerializer for handling F# types within the SDK.
+
+If you need more details about a specific issue, let me know! 
+# User: Are there any untriaged python issues?
+# IssueAgent: There are no untriaged Python issues in the Microsoft semantic-kernel repository. 
+# User: What is the status of issue #10785?
+# IssueAgent: The status of issue #10785 in the Microsoft Semantic Kernel repository is **open**. 
+
+- **Title**: Port dotnet feature: Create MCP Sample
+- **Created at**: March 4, 2025
+- **Comments**: 0
+- **Labels**: python 
+
+You can view the issue [here](https://github.com/microsoft/semantic-kernel/issues/10785). 
+        """
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/mcp/mcp_as_plugin.py

@@ -0,0 +1,115 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+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.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.connectors.mcp import MCPStdioPlugin
+from semantic_kernel.contents import ChatHistory
+
+"""
+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.
+"""
+
+# 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 is also a MCPSsePlugin, which takes a URL.
+    async with MCPStdioPlugin(
+        name="Github",
+        description="Github Plugin",
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-github"],
+    ) 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())