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Sample for MCP in streaming (#42879)
* Sample for MCP in streaming * Update sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_mcp_stream_eventhandler.py Co-authored-by: Copilot <[email protected]> * resolved cmment * Fix capitalization in sample description * Fix comment typo in sample_agents_mcp_stream_iteration.py * Clarify description for MCP stream event handler * Resolved comments --------- Co-authored-by: Copilot <[email protected]>
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‎sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_mcp_stream_eventhandler.py‎

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# pylint: disable=line-too-long,useless-suppression
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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to use Agent operations with the
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Model Context Protocol (MCP) tool in stream with an event handler from the Azure Agents service using a synchronous client.
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To learn more about Model Context Protocol, visit https://modelcontextprotocol.io/
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USAGE:
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python sample_agents_mcp_stream_eventhandler.py
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Before running the sample:
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pip install azure-ai-projects azure-ai-agents>=1.2.0b3 azure-identity --pre
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Set these environment variables with your own values:
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1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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page of your Azure AI Foundry portal.
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2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
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the "Models + endpoints" tab in your Azure AI Foundry project.
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3) MCP_SERVER_URL - The URL of your MCP server endpoint.
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4) MCP_SERVER_LABEL - A label for your MCP server.
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"""
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import os
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from azure.ai.agents import AgentsClient
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from azure.ai.agents.models._models import RunStep, ThreadRun
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from azure.ai.projects import AIProjectClient
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from azure.identity import DefaultAzureCredential
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from azure.ai.agents.models import (
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AgentEventHandler,
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ListSortOrder,
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McpTool,
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MessageDeltaChunk,
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RequiredMcpToolCall,
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RunStepActivityDetails,
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SubmitToolApprovalAction,
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ThreadMessage,
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ToolApproval,
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ToolSet,
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)
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# Get MCP server configuration from environment variables
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mcp_server_url = os.environ.get("MCP_SERVER_URL", "https://gitmcp.io/Azure/azure-rest-api-specs")
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mcp_server_label = os.environ.get("MCP_SERVER_LABEL", "github")
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project_client = AIProjectClient(
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endpoint=os.environ["PROJECT_ENDPOINT"],
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credential=DefaultAzureCredential(),
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)
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# Initialize Agent MCP tool
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mcp_tool = McpTool(
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server_label=mcp_server_label,
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server_url=mcp_server_url,
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allowed_tools=[], # Optional: specify allowed tools
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)
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toolset = ToolSet()
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toolset.add(mcp_tool)
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# You can also add or remove allowed tools dynamically
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search_api_code = "search_azure_rest_api_code"
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mcp_tool.allow_tool(search_api_code)
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print(f"Allowed tools: {mcp_tool.allowed_tools}")
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class MyEventHandler(AgentEventHandler):
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def __init__(self, agents_client: AgentsClient) -> None:
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super().__init__()
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self.agents_client = agents_client
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def on_message_delta(self, delta: "MessageDeltaChunk") -> None:
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print(f"Text delta received: {delta.text}")
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def on_thread_message(self, message: "ThreadMessage") -> None:
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print(f"ThreadMessage created. ID: {message.id}, Status: {message.status}")
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def on_thread_run(self, run: "ThreadRun") -> None:
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if isinstance(run.required_action, SubmitToolApprovalAction):
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tool_calls = run.required_action.submit_tool_approval.tool_calls
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if not tool_calls:
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print("No tool calls provided - cancelling run")
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agents_client.runs.cancel(thread_id=run.thread_id, run_id=run.id)
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return
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tool_approvals = []
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for tool_call in tool_calls:
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if isinstance(tool_call, RequiredMcpToolCall):
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try:
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print(f"Approving tool call: {tool_call}")
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tool_approvals.append(
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ToolApproval(
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tool_call_id=tool_call.id,
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approve=True,
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headers=mcp_tool.headers,
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)
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)
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except Exception as e:
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print(f"Error approving tool_call {tool_call.id}: {e}")
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print(f"tool_approvals: {tool_approvals}")
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if tool_approvals:
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self.agents_client.runs.submit_tool_outputs_stream(
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thread_id=run.thread_id, run_id=run.id, tool_approvals=tool_approvals, event_handler=self
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)
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def on_run_step(self, step: "RunStep") -> None:
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print(f"Step {step.id} status: {step.status}")
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# Check if there are tool calls in the step details
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step_details = step.get("step_details", {})
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tool_calls = step_details.get("tool_calls", [])
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if tool_calls:
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print(" MCP Tool calls:")
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for call in tool_calls:
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print(f" Tool Call ID: {call.get('id')}")
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print(f" Type: {call.get('type')}")
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if isinstance(step_details, RunStepActivityDetails):
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for activity in step_details.activities:
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for function_name, function_definition in activity.tools.items():
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print(
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f' The function {function_name} with description "{function_definition.description}" will be called.:'
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)
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if len(function_definition.parameters) > 0:
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print(" Function parameters:")
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for argument, func_argument in function_definition.parameters.properties.items():
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print(f" {argument}")
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print(f" Type: {func_argument.type}")
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print(f" Description: {func_argument.description}")
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else:
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print("This function has no parameters")
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print() # add an extra newline between steps
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# Create Agent with MCP tool and process Agent run
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with project_client:
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agents_client = project_client.agents
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# Create a new Agent.
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# NOTE: To reuse existing Agent, fetch it with get_agent(agent_id)
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agent = agents_client.create_agent(
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model=os.environ["MODEL_DEPLOYMENT_NAME"],
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name="my-mcp-agent",
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instructions="You are a helpful Agent that can use MCP tools to assist users. Use the available MCP tools to answer questions and perform tasks.",
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toolset=toolset,
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)
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print(f"Created Agent, ID: {agent.id}")
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print(f"MCP Server: {mcp_tool.server_label} at {mcp_tool.server_url}")
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# Create thread for communication
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thread = agents_client.threads.create()
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print(f"Created thread, ID: {thread.id}")
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# Create message to thread
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message = agents_client.messages.create(
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thread_id=thread.id,
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role="user",
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content="Please summarize the Azure REST API specifications Readme",
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)
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print(f"Created message, ID: {message.id}")
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# Create and process Agent run in thread with MCP tools
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mcp_tool.update_headers("SuperSecret", "123456")
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# mcp_tool.set_approval_mode("never") # Uncomment to disable approval requirement
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event_handler = MyEventHandler(agents_client=agents_client)
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with agents_client.runs.stream(thread_id=thread.id, agent_id=agent.id, event_handler=event_handler) as stream:
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stream.until_done()
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# Fetch and log all messages
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messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
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print("\nConversation:")
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print("-" * 50)
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for msg in messages:
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if msg.text_messages:
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last_text = msg.text_messages[-1]
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print(f"{msg.role.upper()}: {last_text.text.value}")
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print("-" * 50)
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# Example of dynamic tool management
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print(f"\nDemonstrating dynamic tool management:")
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print(f"Current allowed tools: {mcp_tool.allowed_tools}")
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# Remove a tool
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try:
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mcp_tool.disallow_tool(search_api_code)
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print(f"After removing {search_api_code}: {mcp_tool.allowed_tools}")
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except ValueError as e:
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print(f"Error removing tool: {e}")
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# Clean-up and delete the Agent once the run is finished.
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# NOTE: Comment out this line if you plan to reuse the Agent later.
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agents_client.delete_agent(agent.id)
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print("Deleted Agent")

‎sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_mcp_stream_iteration.py‎

Lines changed: 186 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,186 @@
1+
# pylint: disable=line-too-long,useless-suppression
2+
# ------------------------------------
3+
# Copyright (c) Microsoft Corporation.
4+
# Licensed under the MIT License.
5+
# ------------------------------------
6+
7+
"""
8+
DESCRIPTION:
9+
This sample demonstrates how to use Agent operations with the
10+
Model Context Protocol (MCP) tool in stream with iteration from the Azure Agents service using a synchronous client.
11+
To learn more about Model Context Protocol, visit https://modelcontextprotocol.io/
12+
13+
USAGE:
14+
python sample_agents_mcp_stream_iteration.py
15+
16+
Before running the sample:
17+
18+
pip install azure-ai-projects azure-ai-agents>=1.2.0b3 azure-identity --pre
19+
20+
Set these environment variables with your own values:
21+
1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
22+
page of your Azure AI Foundry portal.
23+
2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
24+
the "Models + endpoints" tab in your Azure AI Foundry project.
25+
3) MCP_SERVER_URL - The URL of your MCP server endpoint.
26+
4) MCP_SERVER_LABEL - A label for your MCP server.
27+
"""
28+
29+
import os
30+
from azure.ai.agents.models._models import RunStep, ThreadRun
31+
from azure.ai.projects import AIProjectClient
32+
from azure.identity import DefaultAzureCredential
33+
from azure.ai.agents.models import (
34+
ListSortOrder,
35+
McpTool,
36+
MessageDeltaChunk,
37+
RequiredMcpToolCall,
38+
RunStepActivityDetails,
39+
SubmitToolApprovalAction,
40+
ThreadMessage,
41+
ToolApproval,
42+
ToolSet,
43+
)
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45+
# Get MCP server configuration from environment variables
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mcp_server_url = os.environ.get("MCP_SERVER_URL", "https://gitmcp.io/Azure/azure-rest-api-specs")
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mcp_server_label = os.environ.get("MCP_SERVER_LABEL", "github")
48+
49+
project_client = AIProjectClient(
50+
endpoint=os.environ["PROJECT_ENDPOINT"],
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credential=DefaultAzureCredential(),
52+
)
53+
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# Initialize Agent MCP tool
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mcp_tool = McpTool(
56+
server_label=mcp_server_label,
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server_url=mcp_server_url,
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allowed_tools=[], # Optional: specify allowed tools
59+
)
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toolset = ToolSet()
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toolset.add(mcp_tool)
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# You can also add or remove allowed tools dynamically
64+
search_api_code = "search_azure_rest_api_code"
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mcp_tool.allow_tool(search_api_code)
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print(f"Allowed tools: {mcp_tool.allowed_tools}")
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# Create Agent with MCP tool and process Agent run
69+
with project_client:
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agents_client = project_client.agents
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# Create a new Agent.
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# NOTE: To reuse existing Agent, fetch it with get_agent(agent_id)
74+
agent = agents_client.create_agent(
75+
model=os.environ["MODEL_DEPLOYMENT_NAME"],
76+
name="my-mcp-agent",
77+
instructions="You are a helpful Agent that can use MCP tools to assist users. Use the available MCP tools to answer questions and perform tasks.",
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toolset=toolset,
79+
)
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print(f"Created Agent, ID: {agent.id}")
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print(f"MCP Server: {mcp_tool.server_label} at {mcp_tool.server_url}")
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# Create thread for communication
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thread = agents_client.threads.create()
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print(f"Created thread, ID: {thread.id}")
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# Create message to thread
89+
message = agents_client.messages.create(
90+
thread_id=thread.id,
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role="user",
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content="Please summarize the Azure REST API specifications Readme",
93+
)
94+
print(f"Created message, ID: {message.id}")
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# Create and process Agent run in thread with MCP tools
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mcp_tool.update_headers("SuperSecret", "123456")
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# mcp_tool.set_approval_mode("never") # Uncomment to disable approval requirement
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with agents_client.runs.stream(thread_id=thread.id, agent_id=agent.id) as stream:
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for event_type, event_data, _ in stream:
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if isinstance(event_data, MessageDeltaChunk):
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print(f"Text delta received: {event_data.text}")
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elif isinstance(event_data, ThreadRun):
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if isinstance(event_data.required_action, SubmitToolApprovalAction):
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tool_calls = event_data.required_action.submit_tool_approval.tool_calls
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if not tool_calls:
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print("No tool calls provided - cancelling run")
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agents_client.runs.cancel(thread_id=thread.id, run_id=event_data.id)
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break
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tool_approvals = []
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for tool_call in tool_calls:
114+
if isinstance(tool_call, RequiredMcpToolCall):
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try:
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print(f"Approving tool call: {tool_call}")
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tool_approvals.append(
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ToolApproval(
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tool_call_id=tool_call.id,
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approve=True,
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headers=mcp_tool.headers,
122+
)
123+
)
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except Exception as e:
125+
print(f"Error approving tool_call {tool_call.id}: {e}")
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print(f"tool_approvals: {tool_approvals}")
128+
if tool_approvals:
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run = agents_client.runs.submit_tool_outputs_stream(
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thread_id=thread.id, run_id=event_data.id, tool_approvals=tool_approvals, event_handler=stream
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)
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elif isinstance(event_data, RunStep):
133+
print(f"Step {event_data.id} status: {event_data.status}")
134+
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# Check if there are tool calls in the step details
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step_details = event_data.get("step_details", {})
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tool_calls = step_details.get("tool_calls", [])
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if tool_calls:
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print(" MCP Tool calls:")
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for call in tool_calls:
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print(f" Tool Call ID: {call.get('id')}")
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print(f" Type: {call.get('type')}")
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145+
if isinstance(step_details, RunStepActivityDetails):
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for activity in step_details.activities:
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for function_name, function_definition in activity.tools.items():
148+
print(
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f' The function {function_name} with description "{function_definition.description}" will be called.:'
150+
)
151+
if len(function_definition.parameters) > 0:
152+
print(" Function parameters:")
153+
for argument, func_argument in function_definition.parameters.properties.items():
154+
print(f" {argument}")
155+
print(f" Type: {func_argument.type}")
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print(f" Description: {func_argument.description}")
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else:
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print("This function has no parameters")
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160+
print() # add an extra newline between steps
161+
162+
# Fetch and log all messages
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messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
164+
print("\nConversation:")
165+
print("-" * 50)
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for msg in messages:
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if msg.text_messages:
168+
last_text = msg.text_messages[-1]
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print(f"{msg.role.upper()}: {last_text.text.value}")
170+
print("-" * 50)
171+
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# Example of dynamic tool management
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print(f"\nDemonstrating dynamic tool management:")
174+
print(f"Current allowed tools: {mcp_tool.allowed_tools}")
175+
176+
# Remove a tool
177+
try:
178+
mcp_tool.disallow_tool(search_api_code)
179+
print(f"After removing {search_api_code}: {mcp_tool.allowed_tools}")
180+
except ValueError as e:
181+
print(f"Error removing tool: {e}")
182+
183+
# Clean-up and delete the Agent once the run is finished.
184+
# NOTE: Comment out this line if you plan to reuse the Agent later.
185+
agents_client.delete_agent(agent.id)
186+
print("Deleted Agent")

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