Python: Introducing Local MCP Servers (#389)

* mcp parts

* mcp parts 2

* removed structured output in favor of handling in chatresponse, mcp as AITool and running samples

* updated naming

* fixed test

python/samples/getting_started/agents/openai_chat_client/openai_chat_client_with_local_mcp.py

@@ -0,0 +1,76 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import ChatClientAgent, McpStreamableHttpTool
+from agent_framework.openai import OpenAIChatClient
+
+
+async def mcp_tools_on_run_level() -> None:
+    """Example showing MCP tools defined when running the agent."""
+    print("=== Tools Defined on Run Level ===")
+
+    # Tools are provided when running the agent
+    # This means we have to ensure we connect to the MCP server before running the agent
+    # and pass the tools to the run method.
+    async with (
+        McpStreamableHttpTool(
+            name="Microsoft Learn MCP",
+            url="https://learn.microsoft.com/api/mcp",
+        ) as mcp_server,
+        ChatClientAgent(
+            chat_client=OpenAIChatClient(),
+            name="DocsAgent",
+            instructions="You are a helpful assistant that can help with microsoft documentation questions.",
+        ) as agent,
+    ):
+        # First query
+        query1 = "How to create an Azure storage account using az cli?"
+        print(f"User: {query1}")
+        result1 = await agent.run(query1, tools=mcp_server)
+        print(f"{agent.name}: {result1}\n")
+        print("\n=======================================\n")
+        # Second query
+        query2 = "What is Microsoft Semantic Kernel?"
+        print(f"User: {query2}")
+        result2 = await agent.run(query2, tools=mcp_server)
+        print(f"{agent.name}: {result2}\n")
+
+
+async def mcp_tools_on_agent_level() -> None:
+    """Example showing tools defined when creating the agent."""
+    print("=== Tools Defined on Agent Level ===")
+
+    # Tools are provided when creating the agent
+    # The agent can use these tools for any query during its lifetime
+    # The agent will connect to the MCP server through its context manager.
+    async with OpenAIChatClient().create_agent(
+        name="DocsAgent",
+        instructions="You are a helpful assistant that can help with microsoft documentation questions.",
+        tools=McpStreamableHttpTool(  # Tools defined at agent creation
+            name="Microsoft Learn MCP",
+            url="https://learn.microsoft.com/api/mcp",
+        ),
+    ) as agent:
+        # First query
+        query1 = "How to create an Azure storage account using az cli?"
+        print(f"User: {query1}")
+        result1 = await agent.run(query1)
+        print(f"{agent.name}: {result1}\n")
+        print("\n=======================================\n")
+        # Second query
+        query2 = "What is Microsoft Semantic Kernel?"
+        print(f"User: {query2}")
+        result2 = await agent.run(query2)
+        print(f"{agent.name}: {result2}\n")
+
+
+async def main() -> None:
+    print("=== OpenAI Chat Client Agent with MCP Tools Examples ===\n")
+
+    await mcp_tools_on_agent_level()
+    await mcp_tools_on_run_level()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())