Boilerplate MCP Server

Overview

The Boilerplate MCP Server is a ready‑to‑use foundation for building custom Model Context Protocol (MCP) servers with TypeScript. It tackles the common pain points developers face when wiring AI assistants to external APIs: a tangled codebase, fragile error handling, and lack of clear separation between concerns. By providing a clean five‑layer architecture—CLI, tools, controllers, services, and utilities—the boilerplate ensures that each responsibility is isolated, testable, and easily extendable. This structure makes it straightforward to swap out a vendor API or add new tools without touching unrelated parts of the system.

At its core, the server exposes a fully compliant MCP interface that can communicate over two transport modes: STDIO for local AI assistants such as Claude Desktop or Cursor, and a streamable HTTP endpoint that supports Server‑Sent Events for web‑based integrations. The automatic fallback between transports means a single binary can serve both local and remote clients, reducing deployment complexity. The HTTP mode also includes health‑check endpoints and port configuration through environment variables, enabling seamless integration into existing Kubernetes or serverless environments.

Key capabilities of the boilerplate include:

• Type safety and validation: Every request, response, and tool argument is rigorously typed with TypeScript and Zod schemas, catching errors early in the development cycle.
• Structured error handling: All failures surface as standardized MCP errors enriched with contextual logs, simplifying debugging for both developers and AI users.
• Comprehensive tooling: ESLint, Prettier, semantic‑release, and integrated MCP Inspector provide a production‑ready workflow out of the box.
• Extensible IP geolocation example: The included “IP address” tool, resource, and CLI commands demonstrate how to expose a real API (ip‑api.com) as an MCP service, serving as a blueprint for any other external data source.

Real‑world scenarios where this server shines include:

• AI‑powered support desks that need to fetch real‑time inventory or ticket data from legacy systems.
• Conversational agents that must query external weather, finance, or IoT APIs without exposing credentials to the user.
• Internal tooling that allows developers to prototype new AI features locally via STDIO while later deploying the same server for web clients.

By abstracting away transport details and enforcing a disciplined architecture, the Boilerplate MCP Server empowers developers to focus on business logic rather than plumbing. It delivers a robust, maintainable platform that scales from single‑machine prototypes to distributed production deployments, making it an invaluable asset for any team looking to embed AI assistants into their software ecosystem.