Azure Resource Graph MCP Server

Overview

The Azure Resource Graph MCP Server is a specialized Model Context Protocol (MCP) endpoint that bridges AI assistants with the Azure Resource Graph service. It solves a common pain point for cloud engineers and DevOps teams: retrieving detailed, cross‑subscription resource information without writing boilerplate code. By exposing a single, well‑defined tool (), the server allows an AI assistant to ask for resource listings, apply custom Kusto queries, and receive structured JSON results—all within the conversational context of the assistant.

This server is valuable because it abstracts away Azure authentication and query plumbing. It uses DefaultAzureCredential, which automatically picks up credentials from the Azure CLI, environment variables, or managed identities. Developers can therefore focus on crafting queries and interpreting results rather than managing OAuth flows or service principals. The tool’s default query () provides a quick snapshot of an environment, while still permitting arbitrary Kusto queries for advanced filtering or aggregation.

Key features include:

• Cross‑subscription querying: The tool accepts an optional parameter, enabling a single call to span multiple subscriptions if the credentials allow.
• Custom query support: Pass any valid Azure Resource Graph Kusto expression to retrieve exactly the data you need.
• Built‑in authentication: Leverages Azure’s DefaultAzureCredential, simplifying credential management for developers and CI pipelines.
• Simple integration: The MCP server can be launched via standard IDE integrations (Cursor or VS Code), making it immediately available in existing development workflows.

Typical use cases are abundant. A cloud architect can ask the assistant, “Show me all virtual machines in subscription X that are not running,” and receive a ready‑to‑use list. A compliance engineer might request resources that lack a specific tag, while an automation script could trigger remediation actions based on the returned data. In CI/CD pipelines, the server can be invoked to validate resource configurations before deployment.

By integrating this MCP server into an AI workflow, developers gain a powerful, query‑as‑a‑service layer that turns conversational prompts into precise Azure inventory insights. This reduces context switching, eliminates repetitive scripting, and accelerates troubleshooting or governance tasks across complex cloud landscapes.