Overview

The MCP TypeScript Simple Template is a minimal yet fully functional starting point for developers who want to expose custom tools and data sources to AI assistants using the Model Context Protocol (MCP). By bundling a ready‑made MCP server with TypeScript, the template eliminates boilerplate and lets you focus on crafting domain‑specific functionality. The included BMI calculator demonstrates the end‑to‑end flow: defining a tool, validating its input with Zod, and returning structured results that an AI client can consume.

Solving a Common Pain Point

Many teams struggle to get their internal logic or legacy services into an AI workflow. Traditional approaches require building REST APIs, managing authentication, and writing adapters for each assistant platform. The template sidesteps these hurdles by providing a lightweight server that speaks MCP directly over standard I/O or WebSockets. Developers can plug the server into an existing AI pipeline—such as Claude, OpenAI’s GPT‑4o, or a custom in‑house assistant—and immediately expose new capabilities without the overhead of full‑blown microservices.

What It Does and Why It Matters

At its core, the template implements a single MCP server instance. The server registers one sample tool—a BMI calculator—that accepts height and weight, validates the input with Zod (ensuring type safety and clear error messages), and returns a structured JSON response. Because MCP is designed for fine‑grained, context‑aware interactions, the tool’s output can be embedded directly into an assistant’s reply or used to trigger further actions. The server’s design encourages rapid iteration: modify to add new tools, adjust schemas, or hook into external APIs, then rebuild and redeploy with a single command.

Key Features Explained

• TypeScript foundation – Compile‑time safety, IntelliSense, and modern language features.
• Zod validation – Declarative input schemas that automatically generate error responses and improve developer ergonomics.
• Standard I/O communication – The server can be launched as a child process or via Docker, making it trivial to integrate with any system that can pipe JSON.
• Modular tool registration – Each tool is defined via a concise API call, keeping the codebase clean and extensible.
• Zero‑config deployment – No external services or complex setup; just Node.js and npm/yarn.

Real‑World Use Cases

1. Enterprise Knowledge Bases – Expose internal data (HR policies, product specs) as tools that assistants can query on demand.
2. Data‑Driven Decision Support – Build calculators, statistical models, or ETL pipelines that assistants can invoke to generate reports or insights.
3. Prototyping AI Workflows – Rapidly spin up a new tool, test it with an assistant, and iterate without provisioning full infrastructure.
4. Hybrid AI Systems – Combine the template’s tools with large‑language models to create hybrid agents that blend symbolic reasoning and generative capabilities.

Integration into AI Workflows

Once the server is running, an MCP‑compliant client (e.g., Claude or a custom orchestrator) sends a request specifying the tool name and parameters. The server validates, executes the logic, and returns a structured response that the client can embed in its output. Because MCP preserves context across interactions, subsequent messages can refer back to the tool’s result, enabling multi‑step reasoning or stateful conversations.

Standout Advantages

• Simplicity without sacrificing power – The template is deliberately lightweight, yet it uses the full MCP SDK and Zod for robustness.
• Developer‑first design – Clear documentation, TypeScript types, and a single entry point () lower the learning curve.
• Extensibility – Adding new tools is as simple as calling ; the underlying server logic remains unchanged.

In summary, the MCP TypeScript Simple Template equips developers with a streamlined pathway to enrich AI assistants with custom logic. By abstracting away protocol plumbing and offering a ready‑made, type‑safe foundation, it accelerates the delivery of intelligent, context‑aware services across a wide range of domains.