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Improved mcp sample (#44120)
* Imrpoved mcp sample * Resolved comment
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‎sdk/ai/azure-ai-projects/CHANGELOG.md‎

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* Added OpenAPI tool sample. See `sample_agent_openapi.py`.
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* Added OpenAPI with Project Connection sample. See `sample_agent_openapi_with_project_connection.py`.
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* Added SharePoint grounding tool sample. See `sample_agent_sharepoint.py`.
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* Improved MCP client sample showing direct MCP tool invocation. See `samples/mcp_client/sample_mcp_tool_async.py`.
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## 2.0.0b2 (2025-11-14)
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‎sdk/ai/azure-ai-projects/samples/agents/sample_mcp_tool_async_.py‎

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‎sdk/ai/azure-ai-projects/samples/mcp_client/assets/product_info.md‎

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# Information about product item_number: 1
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## Brand
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Contoso Galaxy Innovations
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## Category
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Smart Eyewear
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## Features
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- Augmented Reality interface
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- Voice-controlled AI agent
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- HD video recording with 3D audio
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- UV protection and blue light filtering
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- Wireless charging with extended battery life
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## User Guide
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### 1. Introduction
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Introduction to your new SmartView Glasses
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### 2. Product Overview
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Overview of features and controls
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### 3. Sizing and Fit
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Finding your perfect fit and style adjustments
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### 4. Proper Care and Maintenance
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Cleaning and caring for your SmartView Glasses
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### 5. Break-in Period
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Adjusting to the augmented reality experience
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### 6. Safety Tips
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Safety guidelines for public and private spaces
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### 7. Troubleshooting
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Quick fixes for common issues
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## Warranty Information
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Two-year limited warranty on all electronic components
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## Contact Information
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Customer Support at [email protected]
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## Return Policy
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30-day return policy with no questions asked
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## FAQ
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- How to sync your SmartView Glasses with your devices
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- Troubleshooting connection issues
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- Customizing your augmented reality environment

‎sdk/ai/azure-ai-projects/samples/mcp_client/sample_mcp_tool_async.py‎

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# pylint: disable=line-too-long,useless-suppression
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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to directly interact with MCP (Model Context Protocol) tools
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using the low-level MCP client library to connect to the Foundry Project's MCP tools API:
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{AZURE_AI_PROJECT_ENDPOINT}/mcp_tools?api-version=2025-05-15-preview
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For agent-based MCP tool usage, see samples in samples/agents/tools/sample_agent_mcp.py
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and related files in that directory.
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WORKFLOW:
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This sample demonstrates a typical MCP client workflow:
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1. Establish connection to the Foundry Project MCP endpoint using ClientSession
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2. Initialize the session and discover available tools
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3. Invoke tools programmatically with specific arguments and metadata
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4. Process and save tool outputs (e.g., writing image generation results to a file)
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5. Chain multiple tool calls together (code interpreter → image generation → file search)
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USAGE:
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python sample_mcp_tool_async.py
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Before running the sample:
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pip install "azure-ai-projects>=2.0.0b1" azure-identity python-dotenv mcp
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Set these environment variables with your own values:
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1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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page of your Microsoft Foundry portal.
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2) IMAGE_GEN_DEPLOYMENT_NAME - The deployment name of the image generation model, as found under the "Name" column in
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the "Models + endpoints" tab in your Microsoft Foundry project.
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3) (Optional) LOG_LEVEL - Logging level for HTTP client debugging. Valid values:
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- CRITICAL or 50 - Suppresses all logs except critical errors
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- FATAL - same as CRITICAL
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- ERROR or 40 - Shows errors only
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- WARNING or WARN or 30 - Shows warnings and errors
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- INFO or 20 - Shows informational messages, warnings, and errors
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- DEBUG or 10 - Shows detailed HTTP requests/responses and all other logs
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- NOTSET or 0 - Uses parent logger configuration
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"""
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import asyncio
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import base64
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import os
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import logging
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from dotenv import load_dotenv
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from azure.ai.projects.aio import AIProjectClient
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from azure.identity.aio import DefaultAzureCredential
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from mcp import ClientSession
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from mcp.types import ImageContent
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from mcp.client.streamable_http import streamablehttp_client
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load_dotenv()
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# Configure logging level from environment variable
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# Set LOG_LEVEL=DEBUG to see detailed HTTP requests and responses
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log_level = os.getenv("LOG_LEVEL", "").upper()
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if log_level:
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logging.basicConfig(level=getattr(logging, log_level, logging.CRITICAL))
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# Enable httpx logging to see HTTP requests at the same level
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logging.getLogger("httpx").setLevel(getattr(logging, log_level, logging.CRITICAL))
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endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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async def main():
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async with (
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DefaultAzureCredential() as credential,
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AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
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project_client.get_openai_client() as openai_client,
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streamablehttp_client(
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url=f"{endpoint}/mcp_tools?api-version=2025-05-15-preview",
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headers={"Authorization": f"Bearer {(await credential.get_token('https://ai.azure.com')).token}"},
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) as (read_stream, write_stream, _),
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ClientSession(read_stream, write_stream) as session,
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):
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# Initialize the connection
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await session.initialize()
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# List available tools
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tools = await session.list_tools()
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print(f"Available tools: {[tool.name for tool in tools.tools]}")
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# For each tool, print its details
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for tool in tools.tools:
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print(f"\n\nTool Name: {tool.name}, Input Schema: {tool.inputSchema}")
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# Run the code interpreter tool
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code_interpreter_result = await session.call_tool(
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name="code_interpreter",
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arguments={"code": "print('Hello from Microsoft Foundry MCP Code Interpreter tool!')"},
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)
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print(f"\n\nCode Interpreter Output: {code_interpreter_result.content}")
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# Run the image_generation tool
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image_generation_result = await session.call_tool(
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name="image_generation",
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arguments={"prompt": "Draw a cute puppy riding a skateboard"},
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meta={"imagegen_model_deployment_name": os.getenv("IMAGE_GEN_DEPLOYMENT_NAME", "")},
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)
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# Save the image generation output to a file
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if image_generation_result.content and isinstance(image_generation_result.content[0], ImageContent):
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print("\nDownloading generated image...")
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filename = "puppy.png"
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file_path = os.path.abspath(filename)
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with open(file_path, "wb") as f:
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f.write(base64.b64decode(image_generation_result.content[0].data))
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# Create a vector store
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vector_store = await openai_client.vector_stores.create(
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name="sample_vector_store",
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)
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vector_store_file = await openai_client.vector_stores.files.upload_and_poll(
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vector_store_id=vector_store.id,
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file=open(
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os.path.abspath(os.path.join(os.path.dirname(__file__), "./assets/product_info.md")),
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"rb",
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),
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)
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print(f"\n\nUploaded file, file ID: {vector_store_file.id} to vector store ID: {vector_store.id}")
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# Call the file_search tool
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file_search_result = await session.call_tool(
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name="file_search",
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arguments={"queries": ["What feature does Smart Eyewear offer?"]},
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meta={"vector_store_ids": [vector_store.id]},
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)
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print(f"\n\nFile Search Output: {file_search_result.content}")
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if __name__ == "__main__":
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asyncio.run(main())

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