Azure MCP: VS 2022, VS Code & IntelliJ

Key insights

• Azure MCP Server
  Runs locally to bridge AI assistants and Azure services, giving development tools real-time context about resources and deployments.
  Keeping the server local helps protect sensitive data because it does not need to leave your environment.
• Model Context Protocol (MCP)
  MCP is the standard that sends context from your IDE to AI tools so those tools can act on Azure resources, run commands, or suggest precise changes.
  It supports both interactive prompts and programmatic automation.
• IDE integration
  Works inside Visual Studio 2022, Visual Studio Code, and IntelliJ so developers can manage Azure without leaving their editor.
  Integration surfaces resource details, work items, and deployment status directly to AI assistants for better help and automation.
• Setup in Visual Studio Code
  Install GitHub Copilot and the Azure Copilot extensions, ensure Node.js is installed, then start the MCP server with the recommended command to run locally.
  Point VS Code’s AI assistant settings to the running MCP server to enable context-aware features.
• Setup in IntelliJ and Visual Studio 2022
  In IntelliJ add an MCP Server under Tools or AI Assistant settings and start the local server with the provided start command; then authenticate to connect IDE AI tools to Azure.
  Visual Studio 2022 integrates MCP through its AI assistance settings or corresponding extension to enable the same contextual features.
• Benefits and quick troubleshooting
  Benefits include more accurate AI guidance, multi-language support, and the ability to manage Azure resources from the IDE; it also reduces cloud exposure of sensitive context.
  If you see issues, verify Node.js is installed, confirm the MCP server is running, check authentication and firewall rules, and update your AI extensions and IDE plugins.

The YouTube video published by Microsoft Azure Developers provides a practical walkthrough for installing and using the Azure MCP Server inside three popular development environments: Visual Studio 2022, Visual Studio Code, and IntelliJ. The presenter demonstrates how the Model Context Protocol (MCP) Server connects AI assistants to Azure resources so developers can work with cloud context directly in their IDEs. In this article, we summarize the video’s main steps, explain the benefits, and explore tradeoffs and challenges to help teams decide whether to adopt the workflow. Moreover, we highlight troubleshooting tips that surfaced in the demonstration to reduce friction during setup.

Video overview and purpose

The video aims to show how the Azure MCP Server brings real-time Azure context into AI-driven tools like GitHub Copilot and other AI assistants inside your editor. Featuring Sandeep Sen, the demonstration is concise and focused on practical setup, with clear chapters for each IDE. Consequently, viewers can follow along whether they prefer an integrated experience in an IDE, a lightweight editor, or a JVM-based environment. The session stresses local deployment so sensitive data remains in a developer’s environment rather than flowing to remote services.

Installation steps across IDEs

For Visual Studio Code, the video walks through installing the relevant AI extensions and starting the MCP Server locally, typically using a Node.js-based command to run the server process. After launching the server, the IDE’s AI assistant is configured to use that local endpoint, enabling commands that list and manage Azure resources directly from the editor. In the case of IntelliJ, the presenter shows where to configure the Model Context Protocol under the Tools or AI Assistant settings and how to point the IDE to the running MCP Server. Meanwhile, Visual Studio 2022 is covered at a higher level where integration is available through AI features that consume MCP context, with guidance about checking extension compatibility and workspace settings before attempting a connection.

Benefits and practical use cases

When configured correctly, the Azure MCP Server can deliver context-aware suggestions that go beyond code completion, allowing AI tools to reference deployments, resource details, and automation scripts while you work. This capability streamlines tasks such as deploying a resource, checking configurations, or generating resource-specific scripts without leaving the IDE, which improves productivity for developers who shift between code and cloud tasks. Additionally, because the server runs locally, teams can reduce exposure of internal details to external services and maintain control over logs and telemetry. Altogether, the integration helps teams move faster while keeping contextual accuracy high in AI-generated outputs.

Tradeoffs and technical challenges

Despite clear advantages, adopting a local MCP Server introduces tradeoffs that teams should weigh. Running a server locally adds operational overhead: developers must install Node.js, manage the server process, and keep extensions and the server in sync with updates. Moreover, authentication and permission configuration can become complex, especially in enterprises with federated identity systems, because the MCP workflow requires secure access to Azure resources from within an IDE. Finally, network policies, firewalls, or corporate proxies can disrupt local-to-cloud connections and require additional configuration, which increases setup time for some environments.

Troubleshooting tips and best practices

The video includes several practical troubleshooting tips that help reduce common setup pain points, such as verifying extension versions, confirming that Node.js is installed, and checking that the MCP Server is listening on the expected port. It is also recommended to validate Azure authentication from the command line before connecting the IDE, since many issues stem from expired tokens or insufficient permissions. For teams, it is advisable to document a standard setup procedure and to pin extension versions in shared development environment configurations so that everyone uses a consistent, supported stack. These small steps minimize confusion and improve reproducibility across developer machines.

What organizations should consider next

Organizations interested in this approach should pilot the setup with a small team to measure benefits and uncover integration challenges before rolling it out broadly. Given the balance between security and convenience, some teams will prefer local MCP Servers for sensitive projects, while others may opt for managed cloud-hosted services that reduce operational burden. Finally, continuous monitoring of extension updates and guidance from vendor documentation will help sustain the integration, because tooling and protocols evolve quickly and can change expected behaviors. Overall, the video makes a clear case for experimenting with MCP-enabled workflows while preparing for the practical tradeoffs involved.

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