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Basic Information

The Azure MCP Server implements the Model Context Protocol (MCP) specification to connect AI agents with Azure services. It provides a server and tooling that let agent-enabled clients, such as GitHub Copilot in VS Code, discover and invoke Azure resources and APIs so agents can perform cloud tasks with contextual access. The project is published in Public Preview and can run as a local server, a VS Code extension, a Docker container, or via npx/npm as a custom MCP client. It centralizes access to many Azure products while delegating authentication to the Azure Identity library and documenting telemetry and troubleshooting options. The repository is aimed at developers and integrators who want to enable conversational or programmatic agents to inspect, query, and manage Azure resources without building bespoke connectors.

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1068
App URL
https://github.com/Azure/azure-mcp
Github Repository
https://github.com/Azure/azure-mcp/blob/main/README.md

App Details

Features
Implements the MCP specification to expose Azure tools and resources to agents. Integrations and tools include Azure AI Search, App Configuration, Container Registry, Kubernetes Service, Cosmos DB, Data Explorer, SQL Database and Elastic Pools, Storage, Key Vault, Monitor (logs, metrics, health models), Service Bus, Redis, Foundry, Function Apps, Grafana, Workbooks, Terraform and azd best practices, and more. Supports multiple deployment methods: VS Code extension, npx/npm CLI, Docker container, and custom MCP clients. Provides Azure CLI and Azure Developer CLI (azd) passthrough support. Security and authentication are handled via Azure Identity. Telemetry is enabled by default with an environment variable to opt out. The README includes upgrade and troubleshooting guidance and a contributing guide for adding commands and tools.
Use Cases
The Azure MCP Server lets agents perform real Azure tasks and gather contextual data without manual integration work. Agents can enumerate subscriptions and resource groups, query search indexes and databases, inspect storage accounts and file systems, run KQL against Data Explorer and Log Analytics, manage container registries and AKS clusters, create or list keys and secrets, and execute CLI or azd commands. This accelerates developer workflows, automates cloud discovery and diagnostics from conversational interfaces, and helps generate code and best-practice guidance for deployments. Built-in authentication, telemetry controls, troubleshooting docs, and guidance for enterprise scenarios reduce integration friction and help teams safely pilot agent-enabled automation against Azure resources.

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