Local MCP Server Tutorial

Overview

The Local MCP Server Tutorial demonstrates how to build a lightweight, self‑hosted Model Context Protocol (MCP) server that can be integrated directly into AI assistant workflows such as Claude Desktop. By exposing a set of custom resources, prompts, and tools over the MCP interface, developers can extend an assistant’s capabilities with domain‑specific logic or data without modifying the core model. This approach solves the common problem of hard‑coding application logic into the assistant’s prompt or relying on external APIs that may be unreliable, slow, or costly.

At its core, the server registers three primary types of MCP extensions:

• Resources – lightweight data objects that can be fetched on demand. The example implements a static “Hello, world!” resource and a dynamic greeting template that returns personalized messages for a list of users.
• Prompts – reusable prompt templates that transform user input into a structured set of messages for the model. The tutorial includes a Japanese translation prompt that takes an English string and generates a prompt instructing the model to translate it.
• Tools – executable actions that the assistant can invoke. A BMI calculator is provided, illustrating how the server can perform calculations or any arbitrary logic and return results back to the assistant.

These extensions are defined using TypeScript and the official , which offers type‑safe APIs for declaring schemas (via Zod) and handling resource templates. Once the server is running, it listens on standard I/O and can be launched from a client configuration file. The MCP client (e.g., Claude Desktop) discovers the server, lists its available tools and resources, and can attach them to a conversation. This tight integration enables the assistant to retrieve fresh data or perform computations on‑the‑fly, dramatically increasing flexibility compared to static prompt engineering.

Real‑world scenarios that benefit from this pattern include:

• Enterprise data access – exposing internal databases or APIs as MCP resources so assistants can retrieve up‑to‑date records without embedding credentials in the prompt.
• Domain‑specific calculations – providing tools for finance, engineering, or health metrics that the model can call when needed.
• Multilingual support – defining translation prompts or language‑specific resources that the assistant can use on demand.
• Rapid prototyping – quickly iterating on new features by updating the local server and reloading it in the client, without redeploying a full backend.

Because the server runs locally and communicates over simple I/O streams, developers can debug it with standard tools (e.g., Node’s inspector) and monitor its behavior through a web UI exposed by the MCP inspector. This lightweight, developer‑friendly setup makes it straightforward to prototype and iterate on custom assistant extensions before scaling them out as a public service.