mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
9c57680f00
* Python: Add header_provider to MCPStreamableHTTPTool (#4808) Add a header_provider callback parameter to MCPStreamableHTTPTool that enables injecting dynamic per-request HTTP headers from runtime kwargs (originating from FunctionInvocationContext.kwargs set in agent middleware). The implementation uses contextvars and httpx event hooks to ensure headers are task-local and safe for concurrent tool calls: - header_provider receives the runtime kwargs dict and returns headers - call_tool sets a ContextVar before delegating to MCPTool.call_tool - An httpx request event hook reads from the ContextVar and injects headers Example usage: mcp_tool = MCPStreamableHTTPTool( name="web-api", url="https://api.example.com/mcp", header_provider=lambda kwargs: { "X-Auth-Token": kwargs.get("auth_token", ""), }, ) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #4808: Python: [Bug]: Unable to pass AgentContext to MCPStreamableHTTPTool * Add test for header_provider via FunctionTool.invoke with FunctionInvocationContext Addresses PR review comment: exercises the full pipeline from FunctionInvocationContext.kwargs through FunctionTool.invoke to MCPStreamableHTTPTool.call_tool and header_provider, rather than testing call_tool in isolation. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #4808: review comment fixes * Fix streamable MCP transport defaults Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix Azure AI test client mocks Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix MCP runtime kwarg regressions Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Stabilize MCP tool runtime kwargs Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Use context kwargs in MCP wrappers Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated mcp samples * fix link --------- Co-authored-by: Copilot <copilot@github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
# 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 Agent, tool
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from agent_framework.openai import OpenAIChatClient
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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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/02-agents/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_MODEL": "<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_sessions.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 = Agent(
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client=OpenAIChatClient(),
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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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