Azure MCP Hub

Azure Model Context Protocol (MCP) Hub

Azure MCP Hub is a cloud‑native platform that lets developers host, consume, and extend Model Context Protocol servers on Azure. It solves the common pain point of integrating AI assistants with real APIs—whether those APIs are internal services, database queries, or infrastructure tooling—by providing a standardized interface that AI agents can call with natural language. By running MCP servers as Azure Functions, teams can leverage the platform’s scalability, managed authentication, and native integration with other Azure services.

The hub offers a two‑step workflow. First, developers can spin up their own MCP server in the language of choice (C#, Python, or TypeScript) using Azure Functions. This gives full control over the toolset exposed to an AI agent, from custom business logic to secure data access. Second, the hub supplies a rich set of plug‑and‑play MCP servers that expose common Azure services—such as Redis, PostgreSQL, MySQL, Cosmos DB (MongoDB API), and Azure Data Explorer—as ready‑to‑use tools. These community‑maintained servers allow agents to perform database queries, run KQL analytics, or invoke CLI commands without writing any server code.

Key capabilities include:

• Language‑agnostic SDKs: Official MCP SDKs for C#, Python, TypeScript, and Java let developers build clients or servers in any ecosystem.
• Framework integrations: Native adapters for OpenAI Agents, Semantic Kernel, LangChain.js, Spring AI, and Azure AI Agents make it trivial to add MCP tools to existing agent pipelines.
• Secure, serverless hosting: Azure Functions provides automatic scaling, managed identity support, and seamless integration with Azure Key Vault for secrets.
• Community servers: A growing catalog of open‑source MCP servers covers database access, DevOps tooling, Kubernetes control, and more.

Real‑world use cases span from data‑driven decision support—where an agent can query Azure SQL or ADX on demand—to DevOps automation, where natural language commands trigger GitHub or Azure DevOps workflows. In a customer support scenario, an agent could retrieve ticket data from PostgreSQL and update it via the same MCP interface. Because MCP decouples the AI model from the underlying APIs, teams can evolve their backend services independently of the agent logic.

In practice, an AI workflow might look like this: a user asks, “Show me the last 10 failed deployments.” The agent forwards that query to an MCP server exposing Azure DevOps pipelines, receives structured results, and formats a response. All interactions happen over the same protocol, ensuring consistency, traceability, and auditability across the stack. Azure MCP Hub thus empowers developers to build sophisticated, secure, and maintainable AI agents that can orchestrate real-world services with minimal friction.