Remote MCP Server on Azure Functions (Python)

The Remote MCP Server described in this project addresses a common pain point for developers building AI‑enabled applications: running an MCP (Model Context Protocol) server in a fully managed, secure, and scalable cloud environment. By deploying the server as an Azure Function, teams can expose rich toolsets—such as booking APIs or custom data services—to Claude and other AI assistants without managing infrastructure, while retaining tight control over authentication, network isolation, and logging.

At its core, the server implements the MCP SSE (Server‑Sent Events) endpoint, which serves as the single communication channel for all MCP interactions. Clients send JSON payloads describing resource requests, tool invocations, or prompt completions; the server processes these requests by delegating to underlying Python functions that may call external services (e.g., RapidAPI booking endpoints) or interact with Azure Storage for state persistence. The result is streamed back to the client in real time, enabling low‑latency conversational flows and immediate tool feedback. This architecture eliminates the need for long‑running WebSocket servers or custom polling mechanisms, simplifying deployment and monitoring.

Key capabilities of the server include:

• Secure access via HTTPS and API keys, with optional Azure AD or API Management integration for OAuth flows.
• Network isolation using VNet injection, allowing the server to reach private resources or internal APIs safely.
• Stateful operations through Azure Blob Storage, enabling the server to store and retrieve snippets or session data across invocations.
• Extensible tool registration—developers can add new functions or modify existing ones without changing the client contract.
• Built‑in support for SSE that aligns with Claude’s MCP expectations, ensuring seamless communication.

Typical use cases span travel assistants, e‑commerce bots, or any domain where an AI must interact with third‑party APIs. For instance, a travel booking assistant can call the Booking.com API to fetch attractions or tour reviews, returning structured results directly into the chat. Because the server runs in Azure Functions, it scales automatically with traffic spikes and benefits from Azure’s built‑in monitoring and diagnostics.

Integrating this server into an AI workflow is straightforward: developers add the Function’s SSE URL to their MCP client configuration, authenticate with a system key, and then expose the defined tools to the assistant. From there, agents can invoke tools via natural language prompts, receive streamed responses, and continue the conversation without manual intervention. The combination of serverless scalability, secure networking, and native MCP compatibility makes this solution a powerful foundation for any AI‑driven application that requires reliable, remote tool execution.