Bun MCP SSE Transport

Overview

The bun-mcp-sse-transport server solves a common pain point for developers building AI assistants: delivering real‑time, low‑latency responses from an MCP server to web or native clients without the overhead of WebSockets. By leveraging Server‑Sent Events (SSE), it provides a simple, HTTP‑based channel that is natively supported by browsers and many server frameworks. The transport keeps the flow unidirectional from server to client, while still allowing clients to send JSON‑RPC requests via standard HTTP POST calls. This pattern eliminates the need for long‑polling or complex connection management, making it ideal for lightweight AI workloads that require instant feedback.

In practice, the server creates an SSE endpoint (e.g., ) that clients subscribe to. The server then emits a single message containing the URL where the client should POST JSON‑RPC messages (e.g., ). Once a request arrives, the transport parses it, forwards it to the MCP core (), and streams any resulting responses back over the established SSE stream. Because Bun’s API supports streaming out of the box, the implementation is highly efficient and scales with minimal memory footprint. Developers can drop the transport into any Bun application, expose the two routes, and immediately start receiving live updates from an MCP server.

Key capabilities include:

• Bun‑native integration – uses Bun’s low‑level HTTP and streaming APIs for maximum performance.
• Standard MCP compliance – implements the transport interface expected by the Model Context Protocol, ensuring seamless interoperability with existing MCP tools and libraries.
• Automatic header management – sets the required and CORS headers, reducing boilerplate.
• Robust JSON‑RPC handling – parses incoming POST bodies, validates them against the MCP schema, and forwards them with minimal latency.
• Extensibility – developers can plug additional middleware (e.g., authentication, logging) around the SSE and POST handlers without modifying the core transport logic.

Typical use cases include:

• Real‑time AI chat – a web client subscribes to and streams user inputs via ; responses arrive instantly over the same connection.
• Live data pipelines – an MCP server streams updates from a database or sensor network to dashboards, while clients can push control commands.
• Serverless AI services – the transport can be deployed as a lightweight edge function that forwards calls to a central MCP instance, keeping latency low.

By abstracting the intricacies of SSE management and JSON‑RPC routing, this MCP server lets developers focus on building richer AI experiences. Its minimal footprint and adherence to standard protocols make it a standout choice for anyone working with Bun who needs reliable, real‑time communication between AI assistants and external clients.