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# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to interact with the Foundry Project MCP tool.
USAGE:
python sample_mcp_tool_async.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" azure-identity python-dotenv mcp
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Microsoft Foundry portal.
2) IMAGE_GEN_DEPLOYMENT_NAME - The deployment name of the image generation model, as found under the "Name" column in
the "Models + endpoints" tab in your Microsoft Foundry project.
"""
import asyncio
import os
from dotenv import load_dotenv
from azure.ai.projects.aio import AIProjectClient
from azure.identity.aio import DefaultAzureCredential
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
load_dotenv()
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
async def main():
async with (
DefaultAzureCredential() as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
project_client.get_openai_client() as openai_client,
streamablehttp_client(
url=f"{endpoint}/mcp_tools?api-version=2025-05-15-preview",
headers={"Authorization": f"Bearer {(await credential.get_token('https://ai.azure.com')).token}"},
) as (read_stream, write_stream, _),
):
# Create a session using the client streams
async with ClientSession(read_stream, write_stream) as session:
# Initialize the connection
await session.initialize()
# List available tools
tools = await session.list_tools()
print(f"Available tools: {[tool.name for tool in tools.tools]}")
# For each tool, print its details
for tool in tools.tools:
print(f"\n\nTool Name: {tool.name}, Input Schema: {tool.inputSchema}")
# Run the code interpreter tool
code_interpreter_result = await session.call_tool(
name="code_interpreter",
arguments={"code": "print('Hello from Microsoft Foundry MCP Code Interpreter tool!')"},
)
print(f"\n\nCode Interpreter Output: {code_interpreter_result.content}")
# Run the image_generation tool
image_generation_result = await session.call_tool(
name="image_generation",
arguments={"prompt": "Draw a cute puppy riding a skateboard"},
meta={"imagegen_model_deployment_name": os.getenv("IMAGE_GEN_DEPLOYMENT_NAME", "")},
)
print(f"\n\nImage Generation Output: {image_generation_result.content}")
# Create a vector store
vector_store = await openai_client.vector_stores.create(
name="sample_vector_store",
)
vector_store_file = await openai_client.vector_stores.files.upload_and_poll(
vector_store_id=vector_store.id,
file=open(
os.path.abspath(os.path.join(os.path.dirname(__file__), "./assets/product_info.md")),
"rb",
),
)
print(f"\n\nUploaded file, file ID: {vector_store_file.id} to vector store ID: {vector_store.id}")
# Call the file_search tool
file_search_result = await session.call_tool(
name="file_search",
arguments={"queries": ["What feature does Smart Eyewear offer?"]},
meta={"vector_store_ids": [vector_store.id]},
)
print(f"\n\nFile Search Output: {file_search_result.content}")
if __name__ == "__main__":
asyncio.run(main())
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