db620d33df
CodeQL / Analyze (csharp) (push) Waiting to run
CodeQL / Analyze (python) (push) Waiting to run
dotnet-build-and-test / dotnet-test-functions (push) Has been cancelled
dotnet-build-and-test / paths-filter (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Debug, windows-latest, net9.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, ubuntu-latest, net10.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, ubuntu-latest, net8.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, windows-latest, net472) (push) Has been cancelled
dotnet-build-and-test / dotnet-test (Release, integration, true, ubuntu-latest, net10.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-test (Release, integration, true, windows-latest, net472) (push) Has been cancelled
dotnet-build-and-test / dotnet-foundry-hosted-it (push) Has been cancelled
dotnet-build-and-test / dotnet-build-and-test-check (push) Has been cancelled
dotnet-build-and-test / Integration Test Report (push) Has been cancelled
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
|
|
|
|
from typing import Annotated, Any
|
|
|
|
import anyio
|
|
from agent_framework import Agent, tool
|
|
from agent_framework.openai import OpenAIChatClient
|
|
from dotenv import load_dotenv
|
|
|
|
# Load environment variables from .env file
|
|
load_dotenv()
|
|
|
|
"""
|
|
This sample demonstrates how to expose an Agent as an MCP server.
|
|
|
|
To run this sample, set up your MCP host (like Claude Desktop or VSCode GitHub Copilot Agents)
|
|
with the following configuration:
|
|
```json
|
|
{
|
|
"servers": {
|
|
"agent-framework": {
|
|
"command": "uv",
|
|
"args": [
|
|
"--directory=<path to project>/agent-framework/python/samples/02-agents/mcp",
|
|
"run",
|
|
"agent_as_mcp_server.py"
|
|
],
|
|
"env": {
|
|
"OPENAI_API_KEY": "<OpenAI API key>",
|
|
"OPENAI_MODEL": "<OpenAI Responses model ID>",
|
|
}
|
|
}
|
|
}
|
|
}
|
|
```
|
|
"""
|
|
|
|
|
|
# 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_sessions.py.
|
|
@tool(approval_mode="never_require")
|
|
def get_specials() -> Annotated[str, "Returns the specials from the menu."]:
|
|
return """
|
|
Special Soup: Clam Chowder
|
|
Special Salad: Cobb Salad
|
|
Special Drink: Chai Tea
|
|
"""
|
|
|
|
|
|
@tool(approval_mode="never_require")
|
|
def get_item_price(
|
|
menu_item: Annotated[str, "The name of the menu item."],
|
|
) -> Annotated[str, "Returns the price of the menu item."]:
|
|
return "$9.99"
|
|
|
|
|
|
async def run() -> None:
|
|
# Define an agent
|
|
# Agent's name and description provide better context for AI model
|
|
agent = Agent(
|
|
client=OpenAIChatClient(),
|
|
name="RestaurantAgent",
|
|
description="Answer questions about the menu.",
|
|
tools=[get_specials, get_item_price],
|
|
)
|
|
|
|
# Expose the agent as an MCP server
|
|
server = agent.as_mcp_server()
|
|
|
|
# Run server
|
|
from mcp.server.stdio import stdio_server
|
|
|
|
async def handle_stdin(stdin: Any | None = None, stdout: Any | None = None) -> None:
|
|
async with stdio_server() as (read_stream, write_stream):
|
|
await server.run(read_stream, write_stream, server.create_initialization_options())
|
|
|
|
await handle_stdin()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
anyio.run(run)
|