Overview

The SST Streaming HTTP MCP Server is a lightweight, serverless implementation of the Model Context Protocol (MCP) designed to run on AWS Lambda behind a CloudFront distribution. It enables AI assistants such as Claude to consume real‑time streaming data from external services without the overhead of maintaining persistent connections or complex infrastructure. By exposing a single HTTP endpoint that adheres to the MCP streaming spec, developers can quickly add contextual data streams—like live sensor feeds, real‑time analytics, or continuous log updates—to their AI workflows.

Problem Solved

Traditional MCP deployments often rely on long‑running WebSocket servers or bespoke streaming solutions that require significant operational effort. Managing scaling, latency, and fault tolerance in a distributed environment can become a bottleneck for teams focused on building AI products. The SST server removes this friction by leveraging AWS Lambda’s automatic scaling and CloudFront’s global edge caching, providing a stateless, event‑driven architecture that automatically handles traffic spikes and delivers low‑latency responses to clients worldwide.

Core Functionality

At its heart, the server exposes a endpoint that streams JSON objects to connected clients. Each payload follows the MCP protocol’s “streamable HTTP” format, allowing AI assistants to ingest data incrementally as it arrives. The server is written in TypeScript and bundled with SST’s infrastructure-as-code framework, making deployment as simple as running a single command. Because it is stateless, the same Lambda function can serve multiple clients concurrently without maintaining session data, reducing memory usage and improving cold‑start performance.

Key Features

• Serverless & Scalable – Built on AWS Lambda, the server automatically scales with demand and incurs no cost when idle.
• Global Low‑Latency – CloudFront front‑ends the Lambda, ensuring responses are delivered from edge locations closest to the user.
• MCP‑Compliant Streaming – Implements the “streamable HTTP” transport type, enabling seamless integration with Claude and other MCP‑aware assistants.
• Developer‑Friendly – SST’s monorepo structure and TypeScript tooling provide type safety, linting, and a straightforward deployment workflow.
• Inspector Compatibility – Works out‑of‑the‑box with the UI, allowing developers to test and debug streams locally.

Use Cases & Real‑World Scenarios

• Live Data Feeds – Streaming market data, IoT sensor outputs, or social media feeds to an AI that must react in real time.
• Incremental Log Analysis – Sending continuous log entries to an assistant that performs anomaly detection or generates alerts.
• Interactive Dashboards – Feeding a conversational UI with up‑to‑date metrics, enabling users to ask questions about the latest state.
• Edge‑Computing Applications – Deploying the server in a regionally close CloudFront edge to reduce round‑trip time for latency‑sensitive tasks.

Integration into AI Workflows

Developers can point any MCP‑compatible client—such as the Claude SDK or custom assistants—to the server’s endpoint. The inspector tool demonstrates how to connect, specify routing (), and observe the streaming payloads in real time. Because the server is stateless, it can be combined with other Lambda functions or API Gateways to enrich data before streaming, enabling complex pipelines that feed contextual information directly into the AI’s reasoning loop.

Standout Advantages

• Zero Operational Overhead – No dedicated servers or WebSocket infrastructure; everything runs in the cloud’s managed environment.
• Rapid Deployment – SST’s command packages infrastructure and code together, allowing teams to spin up a production‑ready MCP server in minutes.
• Extensibility – The monorepo structure encourages adding new streams or augmenting existing ones without touching the core server logic.

By abstracting away the complexities of streaming infrastructure, the SST Streaming HTTP MCP Server lets developers focus on crafting richer AI experiences that react to real‑time data, all while enjoying the scalability and reliability of AWS’s serverless platform.