MCP Go SSE Server

Overview

The MCP‑Go SSE server delivers a lightweight, event‑driven implementation of the Model Context Protocol (MCP) that communicates exclusively over Server‑Sent Events (SSE). By exposing MCP resources, tools, prompts and sampling endpoints through a simple HTTP interface, it allows AI assistants such as Claude to interact with external services in real time without the need for custom client libraries. This design removes the overhead of maintaining persistent WebSocket connections while still enabling a push‑style data flow that is well suited to many AI workflows.

The server solves the common problem of bridging external APIs and databases with conversational agents in a way that is both standards‑compliant and developer‑friendly. Developers can author tool definitions, resource schemas, and prompt templates in Go and deploy them as a single binary. The SSE transport automatically streams the MCP protocol messages back to the client, ensuring low‑latency responses and preserving the conversational context across multiple turns. This is particularly valuable for teams that need to expose internal data stores or custom business logic to AI assistants without rewriting the entire integration layer.

Key capabilities include:

• Tool and Resource Exposure – Define reusable tool endpoints that perform CRUD operations on databases or message queues, and expose them through the MCP API.
• Prompt Management – Store and retrieve prompt templates that can be injected into the assistant’s response generation pipeline.
• Sampling Control – Offer fine‑grained sampling parameters (temperature, top‑p, etc.) that the client can adjust on demand.
• SSE Transport – Leverage standard HTTP/1.1 event streams for reliable, ordered delivery of MCP messages, avoiding the complexity of WebSocket handshakes.
• Configuration Flexibility – Simple command‑line flags allow the server to be pointed at any base URL and optionally omit port numbers, making it easy to deploy behind reverse proxies or in cloud environments.

Real‑world use cases abound: a finance team can expose internal risk models as MCP tools, a logistics company can stream live shipment data from Postgres to an AI assistant that schedules pickups, or a DevOps squad can publish NATS event streams through MCP so that an assistant can trigger automated deployments. The server’s example branch () demonstrates exactly how to wire together a Postgres reader and NATS writer, illustrating the ease with which complex data pipelines can be turned into AI‑accessible services.

Because it is written in Go, the binary is statically compiled and highly portable. It integrates seamlessly into existing microservice stacks, allowing developers to incrementally expose new capabilities to AI assistants without re‑architecting their infrastructure. The SSE approach also ensures that the assistant receives updates as soon as they occur, making it ideal for scenarios where real‑time data freshness is critical.