diff --git a/python/samples/getting_started/agents/azure_ai/README.md b/python/samples/getting_started/agents/azure_ai/README.md
index 17a944524a..437094795b 100644
--- a/python/samples/getting_started/agents/azure_ai/README.md
+++ b/python/samples/getting_started/agents/azure_ai/README.md
@@ -20,6 +20,7 @@ This folder contains examples demonstrating different ways to create and use age
 | [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIClient` settings, including project endpoint, model deployment, and credentials rather than relying on environment variable defaults. |
 | [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
 | [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. |
+| [`azure_ai_with_local_mcp.py`](azure_ai_with_local_mcp.py) | Shows how to integrate local Model Context Protocol (MCP) tools with Azure AI agents. |
 | [`azure_ai_with_response_format.py`](azure_ai_with_response_format.py) | Shows how to use structured outputs (response format) with Azure AI agents using Pydantic models to enforce specific response schemas. |
 | [`azure_ai_with_runtime_json_schema.py`](azure_ai_with_runtime_json_schema.py) | Shows how to use structured outputs (response format) with Azure AI agents using a JSON schema to enforce specific response schemas. |
 | [`azure_ai_with_search_context_agentic.py`](../../context_providers/azure_ai_search/azure_ai_with_search_context_agentic.py) | Shows how to use AzureAISearchContextProvider with agentic mode. Uses Knowledge Bases for multi-hop reasoning across documents with query planning. Recommended for most scenarios - slightly slower with more token consumption for query planning, but more accurate results. |
diff --git a/python/samples/getting_started/agents/azure_ai/azure_ai_with_local_mcp.py b/python/samples/getting_started/agents/azure_ai/azure_ai_with_local_mcp.py
new file mode 100644
index 0000000000..dd8cb0abab
--- /dev/null
+++ b/python/samples/getting_started/agents/azure_ai/azure_ai_with_local_mcp.py
@@ -0,0 +1,50 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import MCPStreamableHTTPTool
+from agent_framework.azure import AzureAIClient
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI Agent with Local MCP Example
+
+This sample demonstrates integration of Azure AI Agents with local Model Context Protocol (MCP)
+servers.
+
+Pre-requisites:
+- Make sure to set up the AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME
+  environment variables before running this sample.
+"""
+
+
+async def main() -> None:
+    """Example showing use of Local MCP Tool with AzureAIClient."""
+    print("=== Azure AI Agent with Local MCP Tools Example ===\n")
+
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIClient(async_credential=credential).create_agent(
+            name="DocsAgent",
+            instructions="You are a helpful assistant that can help with Microsoft documentation questions.",
+            tools=MCPStreamableHTTPTool(
+                name="Microsoft Learn MCP",
+                url="https://learn.microsoft.com/api/mcp",
+            ),
+        ) as agent,
+    ):
+        # First query
+        first_query = "How to create an Azure storage account using az cli?"
+        print(f"User: {first_query}")
+        first_result = await agent.run(first_query)
+        print(f"Agent: {first_result}")
+        print("\n=======================================\n")
+        # Second query
+        second_query = "What is Microsoft Agent Framework?"
+        print(f"User: {second_query}")
+        second_result = await agent.run(second_query)
+        print(f"Agent: {second_result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())