Python: fix(declarative): Fix MCP tool connection not passed from YAML to Azure AI agent creation API (#3248)

* fix(declarative): Fix MCP tool connection not passed from YAML

* Add samples to README

* Fix mypy

* Fix mypy again

* Address PR comments
This commit is contained in:
Evan Mattson
2026-01-20 16:24:20 +09:00
committed by GitHub
Unverified
parent 83e8965c8e
commit e0b9be7e08
7 changed files with 779 additions and 110 deletions
@@ -33,7 +33,7 @@ from azure.core.credentials_async import AsyncTokenCredential
from azure.core.exceptions import ResourceNotFoundError
from pydantic import ValidationError
from ._shared import AzureAISettings, create_text_format_config
from ._shared import AzureAISettings, _extract_project_connection_id, create_text_format_config
if sys.version_info >= (3, 13):
from typing import TypeVar # type: ignore # pragma: no cover
@@ -510,6 +510,17 @@ class AzureAIClient(OpenAIBaseResponsesClient[TAzureAIClientOptions], Generic[TA
"""Get MCP tool from HostedMCPTool."""
mcp = MCPTool(server_label=tool.name.replace(" ", "_"), server_url=str(tool.url))
if tool.description:
mcp["server_description"] = tool.description
# Check for project_connection_id in additional_properties (for Azure AI Foundry connections)
project_connection_id = _extract_project_connection_id(tool.additional_properties)
if project_connection_id:
mcp["project_connection_id"] = project_connection_id
elif tool.headers:
# Only use headers if no project_connection_id is available
mcp["headers"] = tool.headers
if tool.allowed_tools:
mcp["allowed_tools"] = list(tool.allowed_tools)
@@ -87,6 +87,37 @@ class AzureAISettings(AFBaseSettings):
model_deployment_name: str | None = None
def _extract_project_connection_id(additional_properties: dict[str, Any] | None) -> str | None:
"""Extract project_connection_id from HostedMCPTool additional_properties.
Checks for both direct 'project_connection_id' key (programmatic usage)
and 'connection.name' structure (declarative/YAML usage).
Args:
additional_properties: The additional_properties dict from a HostedMCPTool.
Returns:
The project_connection_id if found, None otherwise.
"""
if not additional_properties:
return None
# Check for direct project_connection_id (programmatic usage)
project_connection_id = additional_properties.get("project_connection_id")
if isinstance(project_connection_id, str):
return project_connection_id
# Check for connection.name structure (declarative/YAML usage)
if "connection" in additional_properties:
conn = additional_properties["connection"]
if isinstance(conn, dict):
name = conn.get("name")
if isinstance(name, str):
return name
return None
def to_azure_ai_agent_tools(
tools: Sequence[ToolProtocol | MutableMapping[str, Any]] | None,
run_options: dict[str, Any] | None = None,
@@ -322,6 +353,11 @@ def from_azure_ai_tools(tools: Sequence[Tool | dict[str, Any]] | None) -> list[T
if "never" in require_approval:
approval_mode["never_require_approval"] = set(require_approval["never"].get("tool_names", [])) # type: ignore
# Preserve project_connection_id in additional_properties
additional_props: dict[str, Any] | None = None
if project_connection_id := mcp_tool.get("project_connection_id"):
additional_props = {"connection": {"name": project_connection_id}}
agent_tools.append(
HostedMCPTool(
name=mcp_tool.get("server_label", "").replace("_", " "),
@@ -330,6 +366,7 @@ def from_azure_ai_tools(tools: Sequence[Tool | dict[str, Any]] | None) -> list[T
headers=mcp_tool.get("headers"),
allowed_tools=mcp_tool.get("allowed_tools"),
approval_mode=approval_mode, # type: ignore
additional_properties=additional_props,
)
)
elif tool_type == "code_interpreter":
@@ -466,7 +503,13 @@ def _prepare_mcp_tool_for_azure_ai(tool: HostedMCPTool) -> MCPTool:
if tool.description:
mcp["server_description"] = tool.description
if tool.headers:
# Check for project_connection_id in additional_properties (for Azure AI Foundry connections)
project_connection_id = _extract_project_connection_id(tool.additional_properties)
if project_connection_id:
mcp["project_connection_id"] = project_connection_id
elif tool.headers:
# Only use headers if no project_connection_id is available
# Note: Azure AI Agent Service may reject headers with sensitive info
mcp["headers"] = tool.headers
if tool.allowed_tools:
@@ -2,7 +2,7 @@
from collections.abc import Callable, Mapping
from pathlib import Path
from typing import Any, Literal, TypedDict
from typing import Any, Literal, TypedDict, cast
import yaml
from agent_framework import (
@@ -89,6 +89,11 @@ PROVIDER_TYPE_OBJECT_MAPPING: dict[str, ProviderTypeMapping] = {
"name": "AzureAIClient",
"model_id_field": "model_deployment_name",
},
"AzureAI.ProjectProvider": {
"package": "agent_framework.azure",
"name": "AzureAIProjectAgentProvider",
"model_id_field": "model",
},
"Anthropic.Chat": {
"package": "agent_framework.anthropic",
"name": "AnthropicChatClient",
@@ -448,6 +453,175 @@ class AgentFactory:
**chat_options,
)
async def create_agent_from_yaml_path_async(self, yaml_path: str | Path) -> ChatAgent:
"""Async version: Create a ChatAgent from a YAML file path.
Use this method when the provider requires async initialization, such as
AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
Args:
yaml_path: Path to the YAML file representation of a PromptAgent.
Returns:
The ``ChatAgent`` instance created from the YAML file.
Examples:
.. code-block:: python
from agent_framework_declarative import AgentFactory
factory = AgentFactory(
client_kwargs={"credential": credential},
default_provider="AzureAI.ProjectProvider",
)
agent = await factory.create_agent_from_yaml_path_async("agent.yaml")
"""
if not isinstance(yaml_path, Path):
yaml_path = Path(yaml_path)
if not yaml_path.exists():
raise DeclarativeLoaderError(f"YAML file not found at path: {yaml_path}")
yaml_str = yaml_path.read_text()
return await self.create_agent_from_yaml_async(yaml_str)
async def create_agent_from_yaml_async(self, yaml_str: str) -> ChatAgent:
"""Async version: Create a ChatAgent from a YAML string.
Use this method when the provider requires async initialization, such as
AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
Args:
yaml_str: YAML string representation of a PromptAgent.
Returns:
The ``ChatAgent`` instance created from the YAML string.
Examples:
.. code-block:: python
from agent_framework_declarative import AgentFactory
yaml_content = '''
kind: Prompt
name: MyAgent
instructions: You are a helpful assistant.
model:
id: gpt-4o
provider: AzureAI.ProjectProvider
'''
factory = AgentFactory(client_kwargs={"credential": credential})
agent = await factory.create_agent_from_yaml_async(yaml_content)
"""
return await self.create_agent_from_dict_async(yaml.safe_load(yaml_str))
async def create_agent_from_dict_async(self, agent_def: dict[str, Any]) -> ChatAgent:
"""Async version: Create a ChatAgent from a dictionary definition.
Use this method when the provider requires async initialization, such as
AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
Args:
agent_def: Dictionary representation of a PromptAgent.
Returns:
The ``ChatAgent`` instance created from the dictionary.
Examples:
.. code-block:: python
from agent_framework_declarative import AgentFactory
agent_def = {
"kind": "Prompt",
"name": "MyAgent",
"instructions": "You are a helpful assistant.",
"model": {
"id": "gpt-4o",
"provider": "AzureAI.ProjectProvider",
},
}
factory = AgentFactory(client_kwargs={"credential": credential})
agent = await factory.create_agent_from_dict_async(agent_def)
"""
# Set safe_mode context before parsing YAML to control PowerFx environment variable access
_safe_mode_context.set(self.safe_mode)
prompt_agent = agent_schema_dispatch(agent_def)
if not isinstance(prompt_agent, PromptAgent):
raise DeclarativeLoaderError("Only definitions for a PromptAgent are supported for agent creation.")
# Check if we're using a provider-based approach (like AzureAIProjectAgentProvider)
mapping = self._retrieve_provider_configuration(prompt_agent.model) if prompt_agent.model else None
if mapping and mapping["name"] == "AzureAIProjectAgentProvider":
return await self._create_agent_with_provider(prompt_agent, mapping)
# Fall back to standard ChatClient approach
client = self._get_client(prompt_agent)
chat_options = self._parse_chat_options(prompt_agent.model)
if tools := self._parse_tools(prompt_agent.tools):
chat_options["tools"] = tools
if output_schema := prompt_agent.outputSchema:
chat_options["response_format"] = _create_model_from_json_schema("agent", output_schema.to_json_schema())
return ChatAgent(
chat_client=client,
name=prompt_agent.name,
description=prompt_agent.description,
instructions=prompt_agent.instructions,
**chat_options,
)
async def _create_agent_with_provider(self, prompt_agent: PromptAgent, mapping: ProviderTypeMapping) -> ChatAgent:
"""Create a ChatAgent using AzureAIProjectAgentProvider.
This method handles the special case where we use a provider that creates
agents on a remote service (like Azure AI Agent Service) and returns
ChatAgent instances directly.
"""
# Import the provider class
module_name = mapping["package"]
class_name = mapping["name"]
module = __import__(module_name, fromlist=[class_name])
provider_class = getattr(module, class_name)
# Build provider kwargs from client_kwargs and connection info
provider_kwargs: dict[str, Any] = {}
provider_kwargs.update(self.client_kwargs)
# Handle connection settings for the model
if prompt_agent.model and prompt_agent.model.connection:
match prompt_agent.model.connection:
case RemoteConnection() | AnonymousConnection():
if prompt_agent.model.connection.endpoint:
provider_kwargs["project_endpoint"] = prompt_agent.model.connection.endpoint
case ApiKeyConnection():
if prompt_agent.model.connection.endpoint:
provider_kwargs["project_endpoint"] = prompt_agent.model.connection.endpoint
# Create the provider and use it to create the agent
provider = provider_class(**provider_kwargs)
# Parse tools
tools = self._parse_tools(prompt_agent.tools) if prompt_agent.tools else None
# Parse response format
response_format = None
if prompt_agent.outputSchema:
response_format = _create_model_from_json_schema("agent", prompt_agent.outputSchema.to_json_schema())
# Create the agent using the provider
# The provider's create_agent returns a ChatAgent directly
return cast(
ChatAgent,
await provider.create_agent(
name=prompt_agent.name,
model=prompt_agent.model.id if prompt_agent.model else None,
instructions=prompt_agent.instructions,
description=prompt_agent.description,
tools=tools,
response_format=response_format,
),
)
def _get_client(self, prompt_agent: PromptAgent) -> ChatClientProtocol:
"""Create the ChatClientProtocol instance based on the PromptAgent model."""
if not prompt_agent.model:
@@ -594,12 +768,46 @@ class AgentFactory:
)
if not approval_mode:
approval_mode = None
# Handle connection settings
headers: dict[str, str] | None = None
additional_properties: dict[str, Any] | None = None
if tool_resource.connection is not None:
match tool_resource.connection:
case ApiKeyConnection():
if tool_resource.connection.apiKey:
headers = {"Authorization": f"Bearer {tool_resource.connection.apiKey}"}
case RemoteConnection():
additional_properties = {
"connection": {
"kind": tool_resource.connection.kind,
"name": tool_resource.connection.name,
"authenticationMode": tool_resource.connection.authenticationMode,
"endpoint": tool_resource.connection.endpoint,
}
}
case ReferenceConnection():
additional_properties = {
"connection": {
"kind": tool_resource.connection.kind,
"name": tool_resource.connection.name,
"authenticationMode": tool_resource.connection.authenticationMode,
}
}
case AnonymousConnection():
pass
case _:
raise ValueError(f"Unsupported connection kind: {tool_resource.connection.kind}")
return HostedMCPTool(
name=tool_resource.name, # type: ignore
description=tool_resource.description,
url=tool_resource.url, # type: ignore
allowed_tools=tool_resource.allowedTools,
approval_mode=approval_mode,
headers=headers,
additional_properties=additional_properties,
)
case _:
raise ValueError(f"Unsupported tool kind: {tool_resource.kind}")
@@ -692,3 +692,230 @@ model:
assert result.model.connection.apiKey == "secret-key-123"
finally:
_safe_mode_context.reset(token)
class TestAgentFactoryMcpToolConnection:
"""Tests for MCP tool connection handling in AgentFactory._parse_tool."""
def _get_mcp_tools(self, agent):
"""Helper to get MCP tools from agent's default_options."""
from agent_framework import HostedMCPTool
tools = agent.default_options.get("tools", [])
return [t for t in tools if isinstance(t, HostedMCPTool)]
def test_mcp_tool_with_api_key_connection_sets_headers(self):
"""Test that MCP tool with ApiKeyConnection sets headers correctly."""
from unittest.mock import MagicMock
from agent_framework_declarative import AgentFactory
yaml_content = """
kind: Prompt
name: TestAgent
instructions: Test agent
tools:
- kind: mcp
name: my-mcp-tool
url: https://api.example.com/mcp
connection:
kind: key
apiKey: my-secret-api-key
"""
mock_client = MagicMock()
mock_client.create_agent.return_value = MagicMock()
factory = AgentFactory(chat_client=mock_client)
agent = factory.create_agent_from_yaml(yaml_content)
# Find the MCP tool in the agent's tools
mcp_tools = self._get_mcp_tools(agent)
assert len(mcp_tools) == 1
mcp_tool = mcp_tools[0]
# Verify headers are set with the API key
assert mcp_tool.headers is not None
assert mcp_tool.headers == {"Authorization": "Bearer my-secret-api-key"}
def test_mcp_tool_with_remote_connection_sets_additional_properties(self):
"""Test that MCP tool with RemoteConnection sets additional_properties correctly."""
from unittest.mock import MagicMock
from agent_framework_declarative import AgentFactory
yaml_content = """
kind: Prompt
name: TestAgent
instructions: Test agent
tools:
- kind: mcp
name: github-mcp
url: https://api.githubcopilot.com/mcp
connection:
kind: remote
authenticationMode: oauth
name: github-mcp-oauth-connection
"""
mock_client = MagicMock()
mock_client.create_agent.return_value = MagicMock()
factory = AgentFactory(chat_client=mock_client)
agent = factory.create_agent_from_yaml(yaml_content)
# Find the MCP tool in the agent's tools
mcp_tools = self._get_mcp_tools(agent)
assert len(mcp_tools) == 1
mcp_tool = mcp_tools[0]
# Verify additional_properties are set with connection info
assert mcp_tool.additional_properties is not None
assert "connection" in mcp_tool.additional_properties
conn = mcp_tool.additional_properties["connection"]
assert conn["kind"] == "remote"
assert conn["authenticationMode"] == "oauth"
assert conn["name"] == "github-mcp-oauth-connection"
def test_mcp_tool_with_reference_connection_sets_additional_properties(self):
"""Test that MCP tool with ReferenceConnection sets additional_properties correctly."""
from unittest.mock import MagicMock
from agent_framework_declarative import AgentFactory
yaml_content = """
kind: Prompt
name: TestAgent
instructions: Test agent
tools:
- kind: mcp
name: ref-mcp-tool
url: https://api.example.com/mcp
connection:
kind: reference
name: my-connection-ref
target: /connections/my-connection
"""
mock_client = MagicMock()
mock_client.create_agent.return_value = MagicMock()
factory = AgentFactory(chat_client=mock_client)
agent = factory.create_agent_from_yaml(yaml_content)
# Find the MCP tool in the agent's tools
mcp_tools = self._get_mcp_tools(agent)
assert len(mcp_tools) == 1
mcp_tool = mcp_tools[0]
# Verify additional_properties are set with connection info
assert mcp_tool.additional_properties is not None
assert "connection" in mcp_tool.additional_properties
conn = mcp_tool.additional_properties["connection"]
assert conn["kind"] == "reference"
assert conn["name"] == "my-connection-ref"
def test_mcp_tool_with_anonymous_connection_no_headers_or_properties(self):
"""Test that MCP tool with AnonymousConnection doesn't set headers or additional_properties."""
from unittest.mock import MagicMock
from agent_framework_declarative import AgentFactory
yaml_content = """
kind: Prompt
name: TestAgent
instructions: Test agent
tools:
- kind: mcp
name: anon-mcp-tool
url: https://api.example.com/mcp
connection:
kind: anonymous
"""
mock_client = MagicMock()
mock_client.create_agent.return_value = MagicMock()
factory = AgentFactory(chat_client=mock_client)
agent = factory.create_agent_from_yaml(yaml_content)
# Find the MCP tool in the agent's tools
mcp_tools = self._get_mcp_tools(agent)
assert len(mcp_tools) == 1
mcp_tool = mcp_tools[0]
# Verify no headers or additional_properties are set
assert mcp_tool.headers is None
assert mcp_tool.additional_properties is None
def test_mcp_tool_without_connection_preserves_existing_behavior(self):
"""Test that MCP tool without connection works as before (no headers or additional_properties)."""
from unittest.mock import MagicMock
from agent_framework_declarative import AgentFactory
yaml_content = """
kind: Prompt
name: TestAgent
instructions: Test agent
tools:
- kind: mcp
name: simple-mcp-tool
url: https://api.example.com/mcp
approvalMode: never
"""
mock_client = MagicMock()
mock_client.create_agent.return_value = MagicMock()
factory = AgentFactory(chat_client=mock_client)
agent = factory.create_agent_from_yaml(yaml_content)
# Find the MCP tool in the agent's tools
mcp_tools = self._get_mcp_tools(agent)
assert len(mcp_tools) == 1
mcp_tool = mcp_tools[0]
# Verify tool is created correctly without connection
assert mcp_tool.name == "simple-mcp-tool"
assert str(mcp_tool.url) == "https://api.example.com/mcp"
assert mcp_tool.approval_mode == "never_require"
assert mcp_tool.headers is None
assert mcp_tool.additional_properties is None
def test_mcp_tool_with_remote_connection_with_endpoint(self):
"""Test that MCP tool with RemoteConnection including endpoint sets it in additional_properties."""
from unittest.mock import MagicMock
from agent_framework_declarative import AgentFactory
yaml_content = """
kind: Prompt
name: TestAgent
instructions: Test agent
tools:
- kind: mcp
name: endpoint-mcp-tool
url: https://api.example.com/mcp
connection:
kind: remote
authenticationMode: oauth
name: my-oauth-connection
endpoint: https://auth.example.com
"""
mock_client = MagicMock()
mock_client.create_agent.return_value = MagicMock()
factory = AgentFactory(chat_client=mock_client)
agent = factory.create_agent_from_yaml(yaml_content)
# Find the MCP tool in the agent's tools
mcp_tools = self._get_mcp_tools(agent)
assert len(mcp_tools) == 1
mcp_tool = mcp_tools[0]
# Verify additional_properties include endpoint
assert mcp_tool.additional_properties is not None
conn = mcp_tool.additional_properties["connection"]
assert conn["endpoint"] == "https://auth.example.com"
+11
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@@ -180,6 +180,17 @@ The recommended way to use Ollama is via the native `OllamaChatClient` from the
| [`getting_started/context_providers/simple_context_provider.py`](./getting_started/context_providers/simple_context_provider.py) | Simple context provider implementation example |
| [`getting_started/context_providers/aggregate_context_provider.py`](./getting_started/context_providers/aggregate_context_provider.py) | Shows how to combine multiple context providers using an AggregateContextProvider |
## Declarative
| File | Description |
|------|-------------|
| [`getting_started/declarative/azure_openai_responses_agent.py`](./getting_started/declarative/azure_openai_responses_agent.py) | Basic agent using Azure OpenAI with structured responses |
| [`getting_started/declarative/get_weather_agent.py`](./getting_started/declarative/get_weather_agent.py) | Agent with custom function tools using declarative bindings |
| [`getting_started/declarative/inline_yaml.py`](./getting_started/declarative/inline_yaml.py) | Agent created from inline YAML string |
| [`getting_started/declarative/mcp_tool_yaml.py`](./getting_started/declarative/mcp_tool_yaml.py) | MCP tool configuration with API key and Azure Foundry connection auth |
| [`getting_started/declarative/microsoft_learn_agent.py`](./getting_started/declarative/microsoft_learn_agent.py) | Agent with MCP server integration for Microsoft Learn documentation |
| [`getting_started/declarative/openai_responses_agent.py`](./getting_started/declarative/openai_responses_agent.py) | Basic agent using OpenAI directly |
## DevUI
| File | Description |
@@ -0,0 +1,161 @@
# Copyright (c) Microsoft. All rights reserved.
"""
MCP Tool via YAML Declaration
This sample demonstrates how to create agents with MCP (Model Context Protocol)
tools using YAML declarations and the declarative AgentFactory.
Key Features Demonstrated:
1. Loading agent definitions from YAML using AgentFactory
2. Configuring MCP tools with different authentication methods:
- API key authentication (OpenAI.Responses provider)
- Azure AI Foundry connection references (AzureAI.ProjectProvider)
Authentication Options:
- OpenAI.Responses: Supports inline API key auth via headers
- AzureAI.ProjectProvider: Uses Foundry connections for secure credential storage
(no secrets passed in API calls - connection name references pre-configured auth)
Prerequisites:
- `pip install agent-framework-openai agent-framework-declarative --pre`
- For OpenAI example: Set OPENAI_API_KEY and GITHUB_PAT environment variables
- For Azure AI example: Set up a Foundry connection in your Azure AI project
"""
import asyncio
from agent_framework.declarative import AgentFactory
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Example 1: OpenAI.Responses with API key authentication
# Uses inline API key - suitable for OpenAI provider which supports headers
YAML_OPENAI_WITH_API_KEY = """
kind: Prompt
name: GitHubAgent
displayName: GitHub Assistant
description: An agent that can interact with GitHub using the MCP protocol
instructions: |
You are a helpful assistant that can interact with GitHub.
You can search for repositories, read file contents, and check issues.
Always be clear about what operations you're performing.
model:
id: gpt-4o
provider: OpenAI.Responses # Uses OpenAI's Responses API (requires OPENAI_API_KEY env var)
tools:
- kind: mcp
name: github-mcp
description: GitHub MCP tool for repository operations
url: https://api.githubcopilot.com/mcp/
connection:
kind: key
apiKey: =Env.GITHUB_PAT # PowerFx syntax to read from environment variable
approvalMode: never
allowedTools:
- get_file_contents
- get_me
- search_repositories
- search_code
- list_issues
"""
# Example 2: Azure AI with Foundry connection reference
# No secrets in YAML - references a pre-configured Foundry connection by name
# The connection stores credentials securely in Azure AI Foundry
YAML_AZURE_AI_WITH_FOUNDRY_CONNECTION = """
kind: Prompt
name: GitHubAgent
displayName: GitHub Assistant
description: An agent that can interact with GitHub using the MCP protocol
instructions: |
You are a helpful assistant that can interact with GitHub.
You can search for repositories, read file contents, and check issues.
Always be clear about what operations you're performing.
model:
id: gpt-4o
provider: AzureAI.ProjectProvider
tools:
- kind: mcp
name: github-mcp
description: GitHub MCP tool for repository operations
url: https://api.githubcopilot.com/mcp/
connection:
kind: remote
authenticationMode: oauth
name: github-mcp-oauth-connection # References a Foundry connection
approvalMode: never
allowedTools:
- get_file_contents
- get_me
- search_repositories
- search_code
- list_issues
"""
async def run_openai_example():
"""Run the OpenAI.Responses example with API key auth."""
print("=" * 60)
print("Example 1: OpenAI.Responses with API Key Authentication")
print("=" * 60)
factory = AgentFactory(
safe_mode=False, # Allow PowerFx env var resolution (=Env.VAR_NAME)
)
print("\nCreating agent from YAML definition...")
agent = factory.create_agent_from_yaml(YAML_OPENAI_WITH_API_KEY)
async with agent:
query = "What is my GitHub username?"
print(f"\nUser: {query}")
response = await agent.run(query)
print(f"\nAgent: {response.text}")
async def run_azure_ai_example():
"""Run the Azure AI example with Foundry connection.
Prerequisites:
1. Create a Foundry connection named 'github-mcp-oauth-connection' in your
Azure AI project with OAuth credentials for GitHub
2. Set PROJECT_ENDPOINT environment variable to your Azure AI project endpoint
"""
print("=" * 60)
print("Example 2: Azure AI with Foundry Connection Reference")
print("=" * 60)
from azure.identity import DefaultAzureCredential
factory = AgentFactory(client_kwargs={"credential": DefaultAzureCredential()})
print("\nCreating agent from YAML definition...")
# Use async method for provider-based agent creation
agent = await factory.create_agent_from_yaml_async(YAML_AZURE_AI_WITH_FOUNDRY_CONNECTION)
async with agent:
query = "What is my GitHub username?"
print(f"\nUser: {query}")
response = await agent.run(query)
print(f"\nAgent: {response.text}")
async def main():
"""Run the MCP tool examples."""
# Run the OpenAI example
await run_openai_example()
# Run the Azure AI example (uncomment to run)
# Requires: Foundry connection set up and PROJECT_ENDPOINT env var
# await run_azure_ai_example()
if __name__ == "__main__":
asyncio.run(main())
+115 -107
View File
@@ -2294,7 +2294,6 @@ wheels = [
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