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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
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@@ -180,6 +180,17 @@ The recommended way to use Ollama is via the native `OllamaChatClient` from the
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| [`getting_started/context_providers/simple_context_provider.py`](./getting_started/context_providers/simple_context_provider.py) | Simple context provider implementation example |
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| [`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 |
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## Declarative
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| File | Description |
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|------|-------------|
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| [`getting_started/declarative/azure_openai_responses_agent.py`](./getting_started/declarative/azure_openai_responses_agent.py) | Basic agent using Azure OpenAI with structured responses |
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| [`getting_started/declarative/get_weather_agent.py`](./getting_started/declarative/get_weather_agent.py) | Agent with custom function tools using declarative bindings |
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| [`getting_started/declarative/inline_yaml.py`](./getting_started/declarative/inline_yaml.py) | Agent created from inline YAML string |
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| [`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 |
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| [`getting_started/declarative/microsoft_learn_agent.py`](./getting_started/declarative/microsoft_learn_agent.py) | Agent with MCP server integration for Microsoft Learn documentation |
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| [`getting_started/declarative/openai_responses_agent.py`](./getting_started/declarative/openai_responses_agent.py) | Basic agent using OpenAI directly |
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## DevUI
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| File | Description |
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@@ -0,0 +1,161 @@
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# Copyright (c) Microsoft. All rights reserved.
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"""
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MCP Tool via YAML Declaration
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This sample demonstrates how to create agents with MCP (Model Context Protocol)
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tools using YAML declarations and the declarative AgentFactory.
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Key Features Demonstrated:
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1. Loading agent definitions from YAML using AgentFactory
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2. Configuring MCP tools with different authentication methods:
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- API key authentication (OpenAI.Responses provider)
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- Azure AI Foundry connection references (AzureAI.ProjectProvider)
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Authentication Options:
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- OpenAI.Responses: Supports inline API key auth via headers
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- AzureAI.ProjectProvider: Uses Foundry connections for secure credential storage
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(no secrets passed in API calls - connection name references pre-configured auth)
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Prerequisites:
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- `pip install agent-framework-openai agent-framework-declarative --pre`
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- For OpenAI example: Set OPENAI_API_KEY and GITHUB_PAT environment variables
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- For Azure AI example: Set up a Foundry connection in your Azure AI project
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"""
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import asyncio
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from agent_framework.declarative import AgentFactory
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Example 1: OpenAI.Responses with API key authentication
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# Uses inline API key - suitable for OpenAI provider which supports headers
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YAML_OPENAI_WITH_API_KEY = """
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kind: Prompt
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name: GitHubAgent
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displayName: GitHub Assistant
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description: An agent that can interact with GitHub using the MCP protocol
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instructions: |
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You are a helpful assistant that can interact with GitHub.
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You can search for repositories, read file contents, and check issues.
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Always be clear about what operations you're performing.
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model:
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id: gpt-4o
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provider: OpenAI.Responses # Uses OpenAI's Responses API (requires OPENAI_API_KEY env var)
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tools:
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- kind: mcp
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name: github-mcp
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description: GitHub MCP tool for repository operations
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url: https://api.githubcopilot.com/mcp/
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connection:
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kind: key
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apiKey: =Env.GITHUB_PAT # PowerFx syntax to read from environment variable
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approvalMode: never
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allowedTools:
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- get_file_contents
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- get_me
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- search_repositories
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- search_code
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- list_issues
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"""
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# Example 2: Azure AI with Foundry connection reference
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# No secrets in YAML - references a pre-configured Foundry connection by name
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# The connection stores credentials securely in Azure AI Foundry
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YAML_AZURE_AI_WITH_FOUNDRY_CONNECTION = """
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kind: Prompt
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name: GitHubAgent
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displayName: GitHub Assistant
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description: An agent that can interact with GitHub using the MCP protocol
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instructions: |
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You are a helpful assistant that can interact with GitHub.
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You can search for repositories, read file contents, and check issues.
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Always be clear about what operations you're performing.
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model:
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id: gpt-4o
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provider: AzureAI.ProjectProvider
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tools:
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- kind: mcp
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name: github-mcp
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description: GitHub MCP tool for repository operations
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url: https://api.githubcopilot.com/mcp/
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connection:
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kind: remote
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authenticationMode: oauth
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name: github-mcp-oauth-connection # References a Foundry connection
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approvalMode: never
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allowedTools:
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- get_file_contents
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- get_me
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- search_repositories
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- search_code
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- list_issues
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"""
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async def run_openai_example():
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"""Run the OpenAI.Responses example with API key auth."""
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print("=" * 60)
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print("Example 1: OpenAI.Responses with API Key Authentication")
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print("=" * 60)
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factory = AgentFactory(
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safe_mode=False, # Allow PowerFx env var resolution (=Env.VAR_NAME)
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)
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print("\nCreating agent from YAML definition...")
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agent = factory.create_agent_from_yaml(YAML_OPENAI_WITH_API_KEY)
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async with agent:
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query = "What is my GitHub username?"
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print(f"\nUser: {query}")
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response = await agent.run(query)
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print(f"\nAgent: {response.text}")
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async def run_azure_ai_example():
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"""Run the Azure AI example with Foundry connection.
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Prerequisites:
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1. Create a Foundry connection named 'github-mcp-oauth-connection' in your
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Azure AI project with OAuth credentials for GitHub
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2. Set PROJECT_ENDPOINT environment variable to your Azure AI project endpoint
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"""
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print("=" * 60)
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print("Example 2: Azure AI with Foundry Connection Reference")
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print("=" * 60)
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from azure.identity import DefaultAzureCredential
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factory = AgentFactory(client_kwargs={"credential": DefaultAzureCredential()})
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print("\nCreating agent from YAML definition...")
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# Use async method for provider-based agent creation
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agent = await factory.create_agent_from_yaml_async(YAML_AZURE_AI_WITH_FOUNDRY_CONNECTION)
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async with agent:
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query = "What is my GitHub username?"
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print(f"\nUser: {query}")
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response = await agent.run(query)
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print(f"\nAgent: {response.text}")
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async def main():
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"""Run the MCP tool examples."""
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# Run the OpenAI example
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await run_openai_example()
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# Run the Azure AI example (uncomment to run)
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# Requires: Foundry connection set up and PROJECT_ENDPOINT env var
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# await run_azure_ai_example()
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if __name__ == "__main__":
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asyncio.run(main())
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