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Eduard van Valkenburg
a2856d3b92
* restructure: Python samples into progressive 01-05 layout - 01-get-started/: 6 numbered steps (hello agent → hosting) - 02-agents/: all agent concept samples (tools, middleware, providers, etc.) - 03-workflows/: ALL existing workflow samples preserved as-is - 04-hosting/: azure-functions, durabletask, a2a - 05-end-to-end/: demos, evaluation, hosted agents - Old files moved to _to_delete/ for review - Added AGENTS.md with structure documentation - autogen-migration/ and semantic-kernel-migration/ preserved at root * fix: switch to AzureOpenAI Foundry, fix CI failures - Switch all 01-get-started samples to AzureOpenAIResponsesClient with Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT + AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential) - Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes - Fix test paths in packages/ that referenced old getting_started/ dirs: durabletask conftest + streaming test, azurefunctions conftest, devui conftest + capture_messages + openai_sdk_integration - Fix workflow_as_agent_human_in_the_loop.py import (sibling import) - Update hosting READMEs and tool comment paths - Replace root README.md with new structure overview - Update AGENTS.md to document Azure OpenAI Foundry as default provider * cleanup: remove _to_delete folder, copy resource files to active dirs All files in _to_delete/ were either: - Exact duplicates of files in the new structure (240 files) - Same file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since active samples reference them. * fix: address PR review comments, centralize resources, remove root duplicates - Fix type annotation in 04_memory.py (string union -> proper types) - Fix old sample paths in observability files - Fix grammar/spelling in observability samples - Move sample_assets/ and resources/ to shared/ folder - Remove 8 duplicate observability files from 02-agents root - Update resource path references in multimodal_input and provider samples * fix: update broken links from old getting_started paths to new structure - Update relative paths in READMEs: getting_started/ → 01-get-started/, 02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/ - Fix absolute GitHub URLs in package READMEs - Fix broken link in ollama package README * fix: convert absolute GitHub URLs to relative paths for link checker Absolute URLs to python/samples/ on main branch 404 until PR merges. Converted to relative paths that linkspector can verify locally. * fix: update link for handoff sample moved to orchestrations/ * fix: update chatkit-integration README path from demos/ to 05-end-to-end/ * fix: update broken links in orchestrations README to match flat directory structure
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import os
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from agent_framework import Agent, MCPStreamableHTTPTool
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from agent_framework.openai import OpenAIResponsesClient
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from httpx import AsyncClient
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"""
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MCP Authentication Example
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This example demonstrates how to authenticate with MCP servers using API key headers.
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For more authentication examples including OAuth 2.0 flows, see:
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- https://github.com/modelcontextprotocol/python-sdk/tree/main/examples/clients/simple-auth-client
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- https://github.com/modelcontextprotocol/python-sdk/tree/main/examples/servers/simple-auth
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"""
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async def api_key_auth_example() -> None:
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"""Example of using API key authentication with MCP server."""
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# Configuration
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mcp_server_url = os.getenv("MCP_SERVER_URL", "your-mcp-server-url")
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api_key = os.getenv("MCP_API_KEY")
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# Create authentication headers
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# Common patterns:
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# - Bearer token: "Authorization": f"Bearer {api_key}"
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# - API key header: "X-API-Key": api_key
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# - Custom header: "Authorization": f"ApiKey {api_key}"
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auth_headers = {
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"Authorization": f"Bearer {api_key}",
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}
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# Create HTTP client with authentication headers
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http_client = AsyncClient(headers=auth_headers)
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# Create MCP tool with the configured HTTP client
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async with (
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MCPStreamableHTTPTool(
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name="MCP tool",
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description="MCP tool description",
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url=mcp_server_url,
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http_client=http_client, # Pass HTTP client with authentication headers
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) as mcp_tool,
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Agent(
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client=OpenAIResponsesClient(),
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name="Agent",
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instructions="You are a helpful assistant.",
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tools=mcp_tool,
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) as agent,
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):
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query = "What tools are available to you?"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result.text}")
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