MCP Gateway, Server, and Client

The Boilingdata MCP Server and Gateway is a lightweight, transport‑agnostic bridge that lets Claude and other Model Context Protocol (MCP) clients communicate with external services over HTTP Server‑Sent Events (SSE). By wrapping a standard interface in an SSE endpoint, the gateway enables real‑time, streaming interaction between AI assistants and web‑based backends without the need for custom socket or WebSocket implementations. This solves a common pain point for developers who want to expose local MCP tooling—such as custom prompts, resources, or sampling logic—to cloud‑hosted assistants while maintaining low latency and simplicity.

At its core, the server implements the MCP specification’s transport layer: it accepts incoming requests containing serialized MCP messages, forwards them to a local process (the “gateway”), and streams responses back to the client via SSE. This design preserves the full fidelity of MCP’s message structure—commands, responses, and state changes—while leveraging HTTP’s ubiquity. For developers, this means they can run a single Node.js process that acts as both an MCP client and server, simplifying deployment pipelines and enabling integration with existing CI/CD workflows.

Key features include:

• Transport abstraction: Switch seamlessly between local and remote HTTP/SSE without changing the MCP client code.
• Streaming responses: Real‑time delivery of partial results, which is essential for large prompts or long‑running tools.
• Extensible gateway: The module can be customized to route specific MCP commands to different backends or to inject additional metadata.
• Example server/client: A ready‑to‑run example demonstrates how to spin up the gateway, expose it over HTTPS, and connect an MCP client—all with minimal configuration.

Typical use cases span from rapid prototyping of new AI tools to production‑grade deployment of enterprise data connectors. For instance, a company can expose its internal database query engine via MCP, allowing Claude to fetch and format results on demand. Or a data science team can stream large model outputs through the gateway, enabling collaborative debugging and visualization in real time. Because the gateway operates over standard HTTP(S), it integrates naturally with cloud load balancers, API gateways, and authentication services.

What sets this server apart is its simplicity coupled with full MCP compliance. Developers familiar with the protocol can drop this gateway into any stack, trust that message semantics remain intact, and benefit from SSE’s efficient streaming. The result is a robust, low‑overhead bridge that empowers AI assistants to harness external tools and data sources with minimal friction.