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[Agents] add static merge_resources method to McpTool class (#42768)
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‎sdk/ai/azure-ai-agents/CHANGELOG.md‎

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### Features Added
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- Added static merge_resources method to `McpTool` with accompanying sample.
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### Bugs Fixed
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* Fix the issue with logging Agent message, when the message has "in progress" status (related to [issue](https://github.com/Azure/azure-sdk-for-python/issues/42645)).
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* Fix the issue with `RunStepOpenAPIToolCall` logging [issue](https://github.com/Azure/azure-sdk-for-python/issues/42645).
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* Fix the issue with `RunStepMcpToolCall` logging [issue](https://github.com/Azure/azure-sdk-for-python/issues/42689).
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### Sample updates
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- Added sample demonstrating multiple McpTool instance usage.
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## 1.2.0b3 (2025-08-22)
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### Features Added

‎sdk/ai/azure-ai-agents/azure/ai/agents/models/_patch.py‎

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:type tool_call: Any
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"""
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@staticmethod
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def merge_resources(mcp_tools: List["McpTool"]) -> ToolResources:
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"""
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Merge the tool resources from multiple MCP tool instances into a single ToolResources object.
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This is useful when creating a run that should have access to multiple MCP servers at once.
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:param mcp_tools: A list of McpTool instances whose resources will be merged.
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:type mcp_tools: List[McpTool]
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:return: A ToolResources object containing all MCP tool resources from the provided tools.
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:rtype: ToolResources
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:raises ValueError: If the provided list is empty.
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:raises TypeError: If any item in the list is not an instance of McpTool.
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"""
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if not mcp_tools:
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raise ValueError("mcp_tools must be a non-empty list of McpTool instances.")
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flat_resources: List[MCPToolResource] = []
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for tool in mcp_tools:
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if not isinstance(tool, McpTool):
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raise TypeError("All items in mcp_tools must be instances of McpTool.")
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# Combine all MCP resources; duplicates are harmless and can be filtered by the service if needed
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res = tool.resources.get("mcp") # May be a list or a single MCPToolResource depending on model behavior
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if isinstance(res, list):
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flat_resources.extend(cast(List[MCPToolResource], res))
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elif res is not None:
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flat_resources.append(cast(MCPToolResource, res))
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return ToolResources(mcp=flat_resources)
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class AzureFunctionTool(Tool[AzureFunctionToolDefinition]):
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"""

‎sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_multiple_mcp.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 multiple
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Model Context Protocol (MCP) servers 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_multiple_mcp.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_1 - The URL of your first MCP server endpoint.
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4) MCP_SERVER_LABEL_1 - A label for your first MCP server.
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5) MCP_SERVER_URL_2 - The URL of your second MCP server endpoint.
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6) MCP_SERVER_LABEL_2 - A label for your second MCP server.
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"""
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import os, time
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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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ListSortOrder,
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McpTool,
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RequiredMcpToolCall,
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RunStepActivityDetails,
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SubmitToolApprovalAction,
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ToolApproval,
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)
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# Get MCP server configuration from environment variables
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mcp_server_url_1 = os.environ.get("MCP_SERVER_URL_1", "https://gitmcp.io/Azure/azure-rest-api-specs")
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mcp_server_label_1 = os.environ.get("MCP_SERVER_LABEL_1", "github")
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mcp_server_url_2 = os.environ.get("MCP_SERVER_URL_2", "https://learn.microsoft.com/api/mcp")
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mcp_server_label_2 = os.environ.get("MCP_SERVER_LABEL_2", "microsoft_learn")
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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_tool1 = McpTool(
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server_label=mcp_server_label_1,
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server_url=mcp_server_url_1,
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allowed_tools=[], # Optional: specify allowed tools
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)
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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_tool1.allow_tool(search_api_code)
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print(f"Allowed tools: {mcp_tool1.allowed_tools}")
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mcp_tool2 = McpTool(
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server_label=mcp_server_label_2,
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server_url=mcp_server_url_2,
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allowed_tools=["microsoft_docs_search"], # Optional: specify allowed tools
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)
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mcp_tools = [mcp_tool1, mcp_tool2]
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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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tools=[tool.definitions[0] for tool in mcp_tools],
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)
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print(f"Created agent, ID: {agent.id}")
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print(f"MCP Server 1: {mcp_tool1.server_label} at {mcp_tool1.server_url}")
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print(f"MCP Server 2: {mcp_tool2.server_label} at {mcp_tool2.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 and Give me the Azure CLI commands to create an Azure Container App with a managed identity",
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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_tool1.update_headers("SuperSecret", "123456")
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mcp_tool2.set_approval_mode("never") # Disable approval for MS Learn MCP tool
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tool_resources = McpTool.merge_resources(mcp_tools)
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print(tool_resources)
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run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id, tool_resources=tool_resources)
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print(f"Created run, ID: {run.id}")
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while run.status in ["queued", "in_progress", "requires_action"]:
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time.sleep(1)
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run = agents_client.runs.get(thread_id=thread.id, run_id=run.id)
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if run.status == "requires_action" and 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=thread.id, run_id=run.id)
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break
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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_tool1.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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agents_client.runs.submit_tool_outputs(
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thread_id=thread.id, run_id=run.id, tool_approvals=tool_approvals
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)
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print(f"Current run status: {run.status}")
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print(f"Run completed with status: {run.status}")
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if run.status == "failed":
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print(f"Run failed: {run.last_error}")
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# Display run steps and tool calls
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run_steps = agents_client.run_steps.list(thread_id=thread.id, run_id=run.id)
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# Loop through each step
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for step in run_steps:
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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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# 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_tool1.allowed_tools}")
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# Remove a tool
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try:
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mcp_tool1.disallow_tool(search_api_code)
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print(f"After removing {search_api_code}: {mcp_tool1.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")

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