Overview

Mcpgo is a lightweight, Go‑based MCP server that leverages Server‑Sent Events (SSE) to expose AI tool capabilities in real time. It provides a straightforward way for developers to transform any Go application into an MCP‑compatible endpoint, enabling Claude and other AI assistants to discover, query, and invoke services hosted in the Go ecosystem. The server’s primary goal is to bridge the gap between language‑agnostic AI assistants and strongly typed, high‑performance Go backends without requiring complex middleware or custom adapters.

The server solves a common pain point: seamless, low‑latency integration of external tools into an AI workflow. Traditional integrations often rely on REST or gRPC, which introduce request/response overhead and can be cumbersome to maintain across multiple services. By using SSE, Mcpgo delivers a persistent, unidirectional stream that pushes updates and tool results instantly to the client. This eliminates polling, reduces network chatter, and keeps AI assistants responsive even when dealing with long‑running computations or streaming data sources.

Key capabilities of Mcpgo include:

• Resource discovery: The server advertises available tools, prompts, and sampling strategies through a simple JSON schema that Claude can consume automatically.
• Tool invocation: Clients send structured requests over SSE, and Mcpgo executes the corresponding Go function, returning results in a streaming fashion if desired.
• Prompt management: Predefined prompts can be served directly, allowing AI assistants to retrieve context or templates without external storage.
• Sampling controls: The server exposes sampling parameters (temperature, top‑p, etc.) so that AI assistants can fine‑tune the output of language models hosted locally or in other services.

In real‑world scenarios, Mcpgo shines when developers need to expose domain‑specific logic—such as financial calculations, scientific simulations, or enterprise data queries—to an AI assistant. For example, a fintech platform could expose risk‑assessment tools as MCP endpoints, letting Claude generate advisory reports on demand. In scientific research, a simulation library written in Go could be made accessible via Mcpgo, allowing researchers to ask an AI assistant to run parameter sweeps and receive streaming results.

Integration is straightforward: once the Mcpgo server is running, any MCP‑compatible client (Claude, etc.) can discover its capabilities by querying the endpoint. The server’s SSE transport fits naturally into existing AI pipelines, enabling continuous data feeds (e.g., real‑time sensor streams) and interactive tool use without interrupting the assistant’s conversational flow. Its Go foundation ensures high performance, type safety, and easy deployment in containerized or serverless environments.

Overall, Mcpgo offers a minimalistic yet powerful solution for embedding Go services into AI ecosystems. By combining SSE’s real‑time capabilities with the MCP framework, it delivers a developer experience that is both efficient and future‑proof, making it an attractive choice for teams looking to augment AI assistants with custom, high‑performance logic.