add Semantic Kernel MCP Sample using Agents

scenarios/Agents/samples/semantic-kernel-mcp/.env.example

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+AZURE_AI_AGENT_PROJECT_CONNECTION_STRING=...
+AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME=...
+GITHUB_PERSONAL_ACCESS_TOKEN=...
+MCP_SERVER_URL=...

scenarios/Agents/samples/semantic-kernel-mcp/README.md

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+# Using MCP Servers with Azure AI Foundry Agent service and Semantic Kernel
+
+Semantic Kernel has been deeply integrated with the agent service and allows you to easily add additional tools to your Agent. 
+The Model Context Protocol (MCP) is a protocol for exposing tools and other context to models. Semantic Kernel has built-in support for all types of MCP Servers and after reading the definition, it exposes the tools and prompts of the MCP server to the agent that consumes them.
+
+## Setup
+
+1. Install [uv](https://docs.astral.sh/uv/getting-started/installation/) and optionally [docker](https://www.docker.com/products/docker-desktop/)
+1. Ensure you have your Azure AI Foundry Hub and Agent setup done, see [here](../README.md), and add the project connection string and model deployment name to a .env file (or copy the .env.example), with the following names:
+    - AZURE_AI_AGENT_PROJECT_CONNECTION_STRING
+    - AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME
+1. If you want to run the Stdio Server sample: Create a Personal Access Token for Github that has read access to the repositories that you want to interact with, see [the documentation](https://github.com/modelcontextprotocol/servers/tree/main/src/github) on how to create one.
+1. If you want to run the Sse Server sample: add the URL of the MCP server to the .env file, under the `MCP_SERVER_URL` variable.
+1. Navigate into this folder and call `uv run agent_with_stdio_server.py` or `uv run agent_with_sse_server.py` this will automatically create a venv with the required packages installed.
+
+> You can also run the samples with python, just make sure to check the required dependencies in the pyproject.toml file.

scenarios/Agents/samples/semantic-kernel-mcp/agent_with_sse_server.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+from dotenv import load_dotenv
+
+from azure.identity.aio import DefaultAzureCredential
+
+from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings, AzureAIAgentThread
+from semantic_kernel.connectors.mcp import MCPSsePlugin
+
+"""
+The following sample demonstrates how to create a AzureAIAgent that
+uses a SSE based MCP server to add context to the Agent.
+
+It uses the Azure AI Foundry Agent service to create a agent, so make sure to 
+set the required environment variables for the Azure AI Foundry service:
+- AZURE_AI_AGENT_PROJECT_CONNECTION_STRING
+- AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME
+"""
+
+load_dotenv()
+
+
+async def main() -> None:
+    """Main function that creates the plugin, the agent and starts the conversation loop."""
+    async with (
+        # 1. Login to Azure and create a Azure AI Project Client
+        DefaultAzureCredential() as creds,
+        AzureAIAgent.create_client(credential=creds) as client,
+        # 2. Create the MCP plugin
+        MCPSsePlugin(
+            name="mcp",
+            description="MCP Plugin",
+            url=os.getenv("MCP_SERVER_URL"),
+        ) as plugin,
+    ):
+        # 3. Create the agent, with the MCP plugin and the thread
+        agent = AzureAIAgent(
+            client=client,
+            definition=await client.agents.create_agent(
+                model=AzureAIAgentSettings.create().model_deployment_name,
+                name="GithubAgent",
+                instructions="You are a microsoft/semantic-kernel Issue Triage Agent. "
+                "You look at all issues that have the tag: 'triage' and 'python'."
+                "When you find one that is untriaged, you will suggest a new assignee "
+                "based on the issue description, look at recent closed PR's for issues in the same area. "
+                "You will also suggest additional context if needed, like related issues or a bug fix. ",
+            ),
+            plugins=[plugin],  # add the sample plugin to the agent
+        )
+        thread: AzureAIAgentThread | None = None
+        # 4. Print instructions and set the initial user input
+        print("Starting Azure AI Agent with MCP Plugin sample...")
+        print("Once the first prompt is answered, you can further ask questions, use `exit` to exit.")
+        user_input = input("# User: ")
+        try:
+            while user_input.lower() != "exit":
+                # 5. Invoke the agent for a response
+                response = await agent.get_response(messages=user_input, thread=thread)
+                print(f"# {response.name}: {response} ")
+                thread = response.thread
+                # 6. Get a new user input
+                user_input = input("# User: ")
+        finally:
+            # 7. Cleanup: Clear the thread
+            await thread.delete() if thread else None
+            await client.agents.delete_agent(agent.definition.id)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

scenarios/Agents/samples/semantic-kernel-mcp/agent_with_stdio_server.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+from dotenv import load_dotenv
+
+from azure.identity.aio import DefaultAzureCredential
+
+from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings, AzureAIAgentThread
+from semantic_kernel.connectors.mcp import MCPStdioPlugin
+
+"""
+The following sample demonstrates how to create a AzureAIAgent that
+answers questions about Github using a Semantic Kernel Plugin from a MCP server.
+
+It uses the Azure AI Foundry Agent service to create a agent, so make sure to 
+set the required environment variables for the Azure AI Foundry service:
+- AZURE_AI_AGENT_PROJECT_CONNECTION_STRING
+- AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME
+"""
+
+load_dotenv()
+
+
+async def main() -> None:
+    """Main function that creates the plugin, the agent and starts the conversation loop."""
+    async with (
+        # 1. Login to Azure and create a Azure AI Project Client
+        DefaultAzureCredential() as creds,
+        AzureAIAgent.create_client(credential=creds) as client,
+        # 2. Create the MCP plugin
+        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,
+    ):
+        # 3. Create the agent, with the MCP plugin and the thread
+        agent = AzureAIAgent(
+            client=client,
+            definition=await client.agents.create_agent(
+                model=AzureAIAgentSettings.create().model_deployment_name,
+                name="GithubAgent",
+                instructions="You are a microsoft/semantic-kernel Issue Triage Agent. "
+                "You look at all issues that have the tag: 'triage' and 'python'."
+                "When you find one that is untriaged, you will suggest a new assignee "
+                "based on the issue description, look at recent closed PR's for issues in the same area. "
+                "You will also suggest additional context if needed, like related issues or a bug fix. ",
+            ),
+            plugins=[github_plugin],  # add the sample plugin to the agent
+        )
+        thread: AzureAIAgentThread | None = None
+        # 4. Print instructions and set the initial user input
+        print("Starting Azure AI Agent with MCP Plugin sample...")
+        print("Once the first prompt is answered, you can further ask questions, use `exit` to exit.")
+        user_input = "Find the latest untriaged, unassigned issues and suggest new assignees."
+        print(f"# User: {user_input}")
+        try:
+            while user_input.lower() != "exit":
+                # 5. Invoke the agent for a response
+                response = await agent.get_response(messages=user_input, thread=thread)
+                print(f"# {response.name}: {response} ")
+                thread = response.thread
+                # 6. Get a new user input
+                user_input = input("# User: ")
+        finally:
+            # 7. Cleanup: Clear the thread
+            await thread.delete() if thread else None
+            await client.agents.delete_agent(agent.definition.id)
+
+        """
+        Sample output:
+GitHub MCP Server running on stdio
+Starting Azure AI Agent with MCP Plugin sample...
+Once the first prompt is answered, you can further ask questions, use `exit` to exit.
+# User: Find the latest untriaged, unassigned issues and suggest new assignees.
+# GithubAgent: Here are the latest untriaged and unassigned issues that are tagged with Python:
+
+1. **[Issue #11459](https://github.com/microsoft/semantic-kernel/issues/11459)** 
+   - **Title:** Python: Bug: The provided example is incorrect
+   - **Description:** There are apparent mistakes in the provided Python examples concerning shared and 
+   non-shared stateful configurations.
+   - **Assignee Suggestion:** Assign to **eavanvalkenburg** based on prior involvement with Python-related code and 
+   recent PRs focusing on bug fixes.
+
+2. **[Issue #11465](https://github.com/microsoft/semantic-kernel/issues/11465)** 
+   - **Title:** Python: sample using GitHub MCP Server and Azure AI Agent
+   - **Description:** This adds a sample demonstrating how to use MCP tools with the Azure AI Agent.
+   - **Assignee Suggestion:** Assign to **eavanvalkenburg** who is associated with the issue.
+
+### Summary of Suggested Assignees:
+- **Issue #11459**: **eavanvalkenburg**
+- **Issue #11465**: **eavanvalkenburg**
+
+It seems that I cannot update the assignees directly due to authentication issues. You can use this information 
+as you see fit to assign these issues. If you need further assistance or specific context for each issue, 
+please let me know! 
+        """
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

scenarios/Agents/samples/semantic-kernel-mcp/pyproject.toml

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+[project]
+name = "semantic-kernel-mcp"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "semantic-kernel[azure,mcp]>=1.28.0",
+]
+
+[tool.uv]
+prerelease = "allow"