Overview

The MCP-Go SDK is a lightweight, opinionated toolkit for building Model Communication Protocol (MCP) servers and tools in Go. It tackles the friction that developers face when creating custom integrations for AI assistants—whether those assistants are part of an IDE like Cursor, a chatbot platform, or any other AI‑centric workflow. By abstracting away the low‑level details of MCP message framing, transport handling, and JSON schema validation, the SDK lets teams focus on implementing business logic while guaranteeing that their services will interoperate with any MCP‑compliant client.

At its core, the SDK defines a minimal Tool interface that each MCP service must implement. A tool exposes a unique name, a human‑readable description, the JSON schema for its input parameters, and an method that performs the requested action. The return value follows a strict MCP response format—an array of content blocks (currently only plain text is supported, but the structure allows for richer media) and an optional metadata map. This uniform contract enables AI assistants to discover, validate, and invoke tools with confidence, knowing that the responses will be parsed consistently.

The SDK’s transport layer is intentionally pluggable. While the default makes it trivial to run a tool as a command‑line program that communicates via standard input and output, developers can swap in custom transports for sockets, HTTP, or any IPC mechanism. This flexibility allows MCP services to be deployed in diverse environments—from local development setups to cloud‑based microservices—without changing the tool logic.

A standout feature is the seamless integration with Cursor IDE. By adding a simple configuration, developers can expose their Go tools as first‑class commands within the editor. Cursor automatically launches the configured command, negotiates MCP handshakes, and presents the tool’s capabilities to users. This tight coupling streamlines workflows where developers need instant access to custom data transformations, code analysis, or external APIs while coding.

In real‑world scenarios, the MCP-Go SDK shines when teams need to expose internal services—such as database queries, CI/CD triggers, or proprietary APIs—to AI assistants. For example, a data scientist can create an MCP tool that runs complex analytics queries and returns results in natural language. A DevOps engineer can expose a deployment tool that an AI assistant calls to roll out new versions based on conversational prompts. The SDK’s clear separation of concerns, combined with its extensible transport and configuration mechanisms, makes it a powerful foundation for building robust, AI‑enabled tooling ecosystems.