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Adding Foundry Agent MCP Private Connectivity Lab #256
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Labs/ai foundry private mcp
Changes to ensure inter-oprability with WSL and missing items from the VM Start up
| env_var_name = secret_name.replace("-", "_") | ||
| os.environ[env_var_name] = secret.value | ||
| except Exception as e: | ||
| print(f"Error loading secret {secret_name}: {e}") |
Check failure
Code scanning / CodeQL
Clear-text logging of sensitive information High
This expression logs as clear text.
This expression logs as clear text.
sensitive data (secret)
Error loading related location
Loading This expression logs
sensitive data (secret)
Error loading related location
Loading
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It is not printing out the secret value, just the secret key to help troubleshooting in a test environment
…or URL to the final files upload location
New image adding MCP private endpoint diagram and updating notebook f…
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Purpose
This pull request adds a new hands-on lab for demonstrating secure, private connectivity to Azure AI Foundry using Model Context Protocol (MCP), Azure API Management, and Azure Front Door. It introduces comprehensive documentation, sample agent scripts, infrastructure configuration, and a clean-up notebook. The lab guides users through deploying a fully private AI infrastructure, securely running MCP agents, and cleaning up resources.
Key changes:
Documentation and Lab Instructions
README.MDdescribing the architecture, deployment steps, testing scenarios, and resource clean-up for the Azure AI Foundry Private Connectivity Lab. The documentation covers the use of private endpoints, APIM, Front Door, Key Vault, a jumpbox VM, and MCP integration, along with step-by-step instructions for setup and testing.Agent Scripts and Secure Secret Loading
agent/load_env_from_kv.py, a script for securely loading secrets from Azure Key Vault using managed identity and setting them as environment variables for the agent.agent/sample_agents_mcp.py, a comprehensive Python script that demonstrates creating and running an MCP-enabled AI agent, including secure authentication, dynamic tool approval, and step-by-step conversation logging.Infrastructure Configuration
bicepconfig.jsonto enable experimental extensibility features and Microsoft Graph extension support for Bicep deployments, supporting advanced infrastructure automation.Resource Clean-up
clean-up-resources.ipynb, a Jupyter notebook that provides a simple way to clean up all deployed Azure resources after the lab, helping users avoid unnecessary charges.Does this introduce a breaking change?
Pull Request Type
What kind of change does this Pull Request introduce?