Files
Ren Finlayson 539852f81c
Some checks are pending
CodeQL / Analyze (csharp) (push) Waiting to run
CodeQL / Analyze (python) (push) Waiting to run
dotnet-build-and-test / paths-filter (push) Waiting to run
dotnet-build-and-test / dotnet-build-and-test (Debug, windows-latest, net9.0) (push) Blocked by required conditions
dotnet-build-and-test / dotnet-build-and-test (Release, integration, true, ubuntu-latest, net10.0) (push) Blocked by required conditions
dotnet-build-and-test / dotnet-build-and-test (Release, integration, true, windows-latest, net472) (push) Blocked by required conditions
dotnet-build-and-test / dotnet-build-and-test (Release, ubuntu-latest, net8.0) (push) Blocked by required conditions
dotnet-build-and-test / dotnet-build-and-test-check (push) Blocked by required conditions
test
2026-01-24 03:05:12 +11:00

162 lines
5.1 KiB
Python

# 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())