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Python: restructure: Python samples into progressive 01-05 layout (#3862)
* 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file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since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
This commit is contained in:
Eduard van Valkenburg
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# MCP (Model Context Protocol) Examples
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This folder contains examples demonstrating how to work with MCP using Agent Framework.
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## What is MCP?
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The Model Context Protocol (MCP) is an open standard for connecting AI agents to data sources and tools. It enables secure, controlled access to local and remote resources through a standardized protocol.
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## Examples
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| Sample | File | Description |
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|--------|------|-------------|
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| **Agent as MCP Server** | [`agent_as_mcp_server.py`](agent_as_mcp_server.py) | Shows how to expose an Agent Framework agent as an MCP server that other AI applications can connect to |
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| **API Key Authentication** | [`mcp_api_key_auth.py`](mcp_api_key_auth.py) | Demonstrates API key authentication with MCP servers |
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| **GitHub Integration with PAT** | [`mcp_github_pat.py`](mcp_github_pat.py) | Demonstrates connecting to GitHub's MCP server using Personal Access Token (PAT) authentication |
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## Prerequisites
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- `OPENAI_API_KEY` environment variable
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- `OPENAI_RESPONSES_MODEL_ID` environment variable
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For `mcp_github_pat.py`:
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- `GITHUB_PAT` - Your GitHub Personal Access Token (create at https://github.com/settings/tokens)
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# Copyright (c) Microsoft. All rights reserved.
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from typing import Annotated, Any
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import anyio
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from agent_framework import tool
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from agent_framework.openai import OpenAIResponsesClient
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"""
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This sample demonstrates how to expose an Agent as an MCP server.
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To run this sample, set up your MCP host (like Claude Desktop or VSCode GitHub Copilot Agents)
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with the following configuration:
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```json
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{
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"servers": {
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"agent-framework": {
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"command": "uv",
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"args": [
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"--directory=<path to project>/agent-framework/python/samples/getting_started/mcp",
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"run",
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"agent_as_mcp_server.py"
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],
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"env": {
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"OPENAI_API_KEY": "<OpenAI API key>",
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"OPENAI_RESPONSES_MODEL_ID": "<OpenAI Responses model ID>",
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}
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}
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}
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}
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```
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"""
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# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_threads.py.
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@tool(approval_mode="never_require")
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def get_specials() -> Annotated[str, "Returns the specials from the menu."]:
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return """
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Special Soup: Clam Chowder
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Special Salad: Cobb Salad
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Special Drink: Chai Tea
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"""
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@tool(approval_mode="never_require")
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def get_item_price(
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menu_item: Annotated[str, "The name of the menu item."],
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) -> Annotated[str, "Returns the price of the menu item."]:
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return "$9.99"
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async def run() -> None:
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# Define an agent
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# Agent's name and description provide better context for AI model
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agent = OpenAIResponsesClient().as_agent(
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name="RestaurantAgent",
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description="Answer questions about the menu.",
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tools=[get_specials, get_item_price],
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)
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# Expose the agent as an MCP server
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server = agent.as_mcp_server()
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# Run server
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from mcp.server.stdio import stdio_server
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async def handle_stdin(stdin: Any | None = None, stdout: Any | None = None) -> None:
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async with stdio_server() as (read_stream, write_stream):
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await server.run(read_stream, write_stream, server.create_initialization_options())
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await handle_stdin()
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if __name__ == "__main__":
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anyio.run(run)
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# 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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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework import Agent
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from agent_framework.openai import OpenAIResponsesClient
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from dotenv import load_dotenv
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"""
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MCP GitHub Integration with Personal Access Token (PAT)
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This example demonstrates how to connect to GitHub's remote MCP server using a Personal Access
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Token (PAT) for authentication. The agent can use GitHub operations like searching repositories,
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reading files, creating issues, and more depending on how you scope your token.
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Prerequisites:
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1. A GitHub Personal Access Token with appropriate scopes
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- Create one at: https://github.com/settings/tokens
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- For read-only operations, you can use more restrictive scopes
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2. Environment variables:
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- GITHUB_PAT: Your GitHub Personal Access Token (required)
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- OPENAI_API_KEY: Your OpenAI API key (required)
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- OPENAI_RESPONSES_MODEL_ID: Your OpenAI model ID (required)
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"""
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async def github_mcp_example() -> None:
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"""Example of using GitHub MCP server with PAT authentication."""
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# 1. Load environment variables from .env file if present
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load_dotenv()
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# 2. Get configuration from environment
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github_pat = os.getenv("GITHUB_PAT")
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if not github_pat:
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raise ValueError(
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"GITHUB_PAT environment variable must be set. Create a token at https://github.com/settings/tokens"
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)
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# 3. Create authentication headers with GitHub PAT
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auth_headers = {
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"Authorization": f"Bearer {github_pat}",
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}
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# 4. Create agent with the GitHub MCP tool using instance method
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# The MCP tool manages the connection to the MCP server and makes its tools available
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# Set approval_mode="never_require" to allow the MCP tool to execute without approval
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client = OpenAIResponsesClient()
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github_mcp_tool = client.get_mcp_tool(
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server_label="GitHub",
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server_url="https://api.githubcopilot.com/mcp/",
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headers=auth_headers,
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require_approval="never",
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)
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# 5. Create agent with the GitHub MCP tool
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async with Agent(
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client=client,
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name="GitHubAgent",
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instructions=(
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"You are a helpful assistant that can help users interact with GitHub. "
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"You can search for repositories, read file contents, check issues, and more. "
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"Always be clear about what operations you're performing."
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),
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tools=github_mcp_tool,
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) as agent:
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# Example 1: Get authenticated user information
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query1 = "What is my GitHub username and tell me about my account?"
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print(f"\nUser: {query1}")
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result1 = await agent.run(query1)
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print(f"Agent: {result1.text}")
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# Example 2: List my repositories
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query2 = "List all the repositories I own on GitHub"
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print(f"\nUser: {query2}")
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result2 = await agent.run(query2)
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print(f"Agent: {result2.text}")
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if __name__ == "__main__":
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asyncio.run(github_mcp_example())
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