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‎.github/CODEOWNERS‎

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# Default owner for everything, unless overridden by a more specific rule.
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* @Azure-Samples/azure-ai-samples-maintainers
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#### files referenced in docs (DO NOT EDIT, except for Docs team!!!) ###################
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/scenarios/projects/basic/chat-simple.py @azure-samples/AI-Platform-Docs
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/scenarios/projects/basic/chat-template.py @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/assets/chat_eval_data.jsonl @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/assets/grounded_chat.prompty @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/assets/intent_mapping.prompty @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/assets/products.csv @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/chat_with_products.py @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/config.py @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/create_search_index.py @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/evaluate.py @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/get_product_documents.py @azure-samples/AI-Platform-Docs
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/scenarios/rag/custom-rag-app/requirements.txt @azure-samples/AI-Platform-Docs
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/scenarios/langchain/getting-started-with-langchain-chat-models.ipynb @azure-samples/AI-Platform-Docs
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/scenarios/langchain/getting-started-with-langchain-embeddings.ipynb @azure-samples/AI-Platform-Docs

‎.github/scripts/build_agent_setup_bicep.py‎

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sys.exit(exit_code)
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def get_main_bicep_files(modified_files: List[str]) -> List[Path]:
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def get_main_bicep_files(modified_files: List[Path]) -> List[Path]:
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"""Finds unique folders with modified files and ensures 'main.bicep' exists in each."""
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modified_folders = {Path(f).parent for f in modified_files}
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return [folder / "main.bicep" for folder in modified_folders if (folder / "main.bicep").exists()]

‎.pre-commit-config.yaml‎

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language: system
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types_or: [jupyter]
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minimum_pre_commit_version: 2.9.2
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args: ["-m", "tox", "-qqq", "run", "-e", "nb-clean", "--", "clean", "--preserve-cell-metadata", "tags", "--"]
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args: ["-m", "tox", "-qqq", "run", "-e", "nb-clean", "--", "clean", "--preserve-cell-metadata", "tags", "name", "--"]
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- id: ruff
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name: ruff
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description: "Run 'ruff' for extremely fast Python linting"

‎scenarios/Agents/README.md‎

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‎scenarios/Agents/samples/semantic-kernel-mcp/.env.example‎

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AZURE_AI_AGENT_PROJECT_CONNECTION_STRING=...
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AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME=...
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GITHUB_PERSONAL_ACCESS_TOKEN=...
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MCP_SERVER_URL=...

‎scenarios/Agents/samples/semantic-kernel-mcp/README.md‎

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# Using MCP Servers with Azure AI Foundry Agent service and Semantic Kernel
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Semantic Kernel has been deeply integrated with the agent service and allows you to easily add additional tools to your Agent.
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The Model Context Protocol (MCP) is a protocol for exposing tools and other context to models. Semantic Kernel has built-in support for all types of MCP Servers and after reading the definition, it exposes the tools and prompts of the MCP server to the agent that consumes them.
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## Setup
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1. Install [uv](https://docs.astral.sh/uv/getting-started/installation/) and optionally [docker](https://www.docker.com/products/docker-desktop/)
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1. Ensure you have your Azure AI Foundry Hub and Agent setup done, see [here](../README.md), and add the project connection string and model deployment name to a .env file (or copy the .env.example), with the following names:
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- AZURE_AI_AGENT_PROJECT_CONNECTION_STRING
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- AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME
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1. If you want to run the Stdio Server sample: Create a Personal Access Token for Github that has read access to the repositories that you want to interact with, see [the documentation](https://github.com/modelcontextprotocol/servers/tree/main/src/github) on how to create one.
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1. If you want to run the Sse Server sample: add the URL of the MCP server to the .env file, under the `MCP_SERVER_URL` variable.
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1. Navigate into this folder and call `uv run agent_with_stdio_server.py` or `uv run agent_with_sse_server.py` this will automatically create a venv with the required packages installed.
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> You can also run the samples with python, just make sure to check the required dependencies in the pyproject.toml file.

‎scenarios/Agents/samples/semantic-kernel-mcp/agent_with_sse_server.py‎

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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 dotenv import load_dotenv
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from azure.identity.aio import DefaultAzureCredential
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from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings, AzureAIAgentThread
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from semantic_kernel.connectors.mcp import MCPSsePlugin
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"""
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The following sample demonstrates how to create a AzureAIAgent that
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uses a SSE based MCP server to add context to the Agent.
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It uses the Azure AI Foundry Agent service to create a agent, so make sure to
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set the required environment variables for the Azure AI Foundry service:
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- AZURE_AI_AGENT_PROJECT_CONNECTION_STRING
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- AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME
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"""
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load_dotenv()
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async def main() -> None:
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"""Main function that creates the plugin, the agent and starts the conversation loop."""
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async with (
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# 1. Login to Azure and create a Azure AI Project Client
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DefaultAzureCredential() as creds,
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AzureAIAgent.create_client(credential=creds) as client,
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# 2. Create the MCP plugin
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MCPSsePlugin(
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name="mcp",
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description="MCP Plugin",
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url=os.getenv("MCP_SERVER_URL"),
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) as plugin,
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):
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# 3. Create the agent, with the MCP plugin and the thread
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agent = AzureAIAgent(
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client=client,
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definition=await client.agents.create_agent(
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model=AzureAIAgentSettings.create().model_deployment_name,
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name="GithubAgent",
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instructions="You are a microsoft/semantic-kernel Issue Triage Agent. "
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"You look at all issues that have the tag: 'triage' and 'python'."
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"When you find one that is untriaged, you will suggest a new assignee "
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"based on the issue description, look at recent closed PR's for issues in the same area. "
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"You will also suggest additional context if needed, like related issues or a bug fix. ",
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),
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plugins=[plugin], # add the sample plugin to the agent
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)
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thread: AzureAIAgentThread | None = None
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# 4. Print instructions and set the initial user input
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print("Starting Azure AI Agent with MCP Plugin sample...")
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print("Once the first prompt is answered, you can further ask questions, use `exit` to exit.")
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user_input = input("# User: ")
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try:
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while user_input.lower() != "exit":
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# 5. Invoke the agent for a response
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response = await agent.get_response(messages=user_input, thread=thread)
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print(f"# {response.name}: {response} ")
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thread = response.thread
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# 6. Get a new user input
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user_input = input("# User: ")
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finally:
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# 7. Cleanup: Clear the thread
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await thread.delete() if thread else None
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await client.agents.delete_agent(agent.definition.id)
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if __name__ == "__main__":
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asyncio.run(main())

‎scenarios/Agents/samples/semantic-kernel-mcp/agent_with_stdio_server.py‎

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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 dotenv import load_dotenv
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from azure.identity.aio import DefaultAzureCredential
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from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings, AzureAIAgentThread
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from semantic_kernel.connectors.mcp import MCPStdioPlugin
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"""
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The following sample demonstrates how to create a AzureAIAgent that
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answers questions about Github using a Semantic Kernel Plugin from a MCP server.
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It uses the Azure AI Foundry Agent service to create a agent, so make sure to
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set the required environment variables for the Azure AI Foundry service:
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- AZURE_AI_AGENT_PROJECT_CONNECTION_STRING
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- AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME
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"""
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load_dotenv()
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async def main() -> None:
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"""Main function that creates the plugin, the agent and starts the conversation loop."""
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async with (
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# 1. Login to Azure and create a Azure AI Project Client
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DefaultAzureCredential() as creds,
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AzureAIAgent.create_client(credential=creds) as client,
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# 2. Create the MCP plugin
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MCPStdioPlugin(
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name="github",
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description="Github Plugin",
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command="docker",
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args=["run", "-i", "--rm", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN", "ghcr.io/github/github-mcp-server"],
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env={"GITHUB_PERSONAL_ACCESS_TOKEN": os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN")},
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) as github_plugin,
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):
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# 3. Create the agent, with the MCP plugin and the thread
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agent = AzureAIAgent(
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client=client,
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definition=await client.agents.create_agent(
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model=AzureAIAgentSettings.create().model_deployment_name,
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name="GithubAgent",
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instructions="You are a microsoft/semantic-kernel Issue Triage Agent. "
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"You look at all issues that have the tag: 'triage' and 'python'."
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"When you find one that is untriaged, you will suggest a new assignee "
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"based on the issue description, look at recent closed PR's for issues in the same area. "
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"You will also suggest additional context if needed, like related issues or a bug fix. ",
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),
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plugins=[github_plugin], # add the sample plugin to the agent
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)
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thread: AzureAIAgentThread | None = None
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# 4. Print instructions and set the initial user input
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print("Starting Azure AI Agent with MCP Plugin sample...")
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print("Once the first prompt is answered, you can further ask questions, use `exit` to exit.")
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user_input = "Find the latest untriaged, unassigned issues and suggest new assignees."
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print(f"# User: {user_input}")
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try:
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while user_input.lower() != "exit":
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# 5. Invoke the agent for a response
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response = await agent.get_response(messages=user_input, thread=thread)
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print(f"# {response.name}: {response} ")
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thread = response.thread
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# 6. Get a new user input
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user_input = input("# User: ")
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finally:
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# 7. Cleanup: Clear the thread
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await thread.delete() if thread else None
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await client.agents.delete_agent(agent.definition.id)
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"""
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Sample output:
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GitHub MCP Server running on stdio
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Starting Azure AI Agent with MCP Plugin sample...
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Once the first prompt is answered, you can further ask questions, use `exit` to exit.
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# User: Find the latest untriaged, unassigned issues and suggest new assignees.
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# GithubAgent: Here are the latest untriaged and unassigned issues that are tagged with Python:
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1. **[Issue #11459](https://github.com/microsoft/semantic-kernel/issues/11459)**
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- **Title:** Python: Bug: The provided example is incorrect
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- **Description:** There are apparent mistakes in the provided Python examples concerning shared and
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non-shared stateful configurations.
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- **Assignee Suggestion:** Assign to **eavanvalkenburg** based on prior involvement with Python-related code and
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recent PRs focusing on bug fixes.
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2. **[Issue #11465](https://github.com/microsoft/semantic-kernel/issues/11465)**
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- **Title:** Python: sample using GitHub MCP Server and Azure AI Agent
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- **Description:** This adds a sample demonstrating how to use MCP tools with the Azure AI Agent.
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- **Assignee Suggestion:** Assign to **eavanvalkenburg** who is associated with the issue.
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### Summary of Suggested Assignees:
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- **Issue #11459**: **eavanvalkenburg**
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- **Issue #11465**: **eavanvalkenburg**
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It seems that I cannot update the assignees directly due to authentication issues. You can use this information
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as you see fit to assign these issues. If you need further assistance or specific context for each issue,
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please let me know!
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"""
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if __name__ == "__main__":
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asyncio.run(main())

‎scenarios/Agents/samples/semantic-kernel-mcp/pyproject.toml‎

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[project]
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name = "semantic-kernel-mcp"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"semantic-kernel[azure,mcp]>=1.28.0",
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]
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[tool.uv]
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prerelease = "allow"

‎scenarios/Agents/setup/basic-agent-identity/azuredeploy.json‎

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"_generator": {
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"name": "bicep",
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"version": "0.33.93.31351",
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"templateHash": "9343374115382580767"
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"templateHash": "3556712594165874979"
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}
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},
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"parameters": {
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"_generator": {
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"name": "bicep",
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"version": "0.33.93.31351",
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"templateHash": "13463183484961318850"
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"templateHash": "10842040050834008228"
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}
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},
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"parameters": {
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},
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"properties": {
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"customSubDomainName": "[toLower(format('{0}', toLower(parameters('aiServicesName'))))]",
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"apiProperties": {
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"statisticsEnabled": false
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},
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"publicNetworkAccess": "Enabled"
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}
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},

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