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MCP JS Server Template

MCP Server

Starter kit for building custom MCP servers in JavaScript

Stale(50)
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Updated Jul 3, 2025

About

A lightweight template that sets up a Model Context Protocol server using @modelcontextprotocol/sdk, providing basic tools like about and hello. Ideal for developers looking to create custom MCP servers with minimal boilerplate.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

Overview

The MCP JS Server Template is a lightweight starting point for developers who want to expose custom tools and data sources to Claude Desktop via the Model Context Protocol (MCP). By providing a minimal yet complete implementation, it removes the boilerplate that typically accompanies MCP server development and allows teams to focus on business logic instead of protocol plumbing. The template ships with a ready‑to‑run JavaScript file that follows the latest MCP protocol version (2024-11-05) and leverages the official package for reliable server initialization, error handling, and logging.

What problem does it solve?

Many AI‑centric applications require a bridge between internal APIs, legacy systems, or domain‑specific services and the assistant. Building such a bridge from scratch can be error‑prone, especially when dealing with the intricacies of MCP message framing, authentication, and resource discovery. This template eliminates those friction points by providing a working skeleton that already implements protocol compliance, request routing, and basic diagnostics. Developers can simply add new tools or modify existing ones without wrestling with the underlying protocol mechanics.

Core functionality and value

  • Standard MCP initialization: The server automatically registers itself with Claude Desktop, announces its name and version, and advertises the tools it offers.
  • Built‑in tooling: Two example tools—, which returns server metadata, and , which greets a user—demonstrate how tool definitions translate into actionable logic.
  • Robust error handling: Unexpected inputs or internal failures are caught and logged, ensuring that the assistant receives clear failure messages rather than silent crashes.
  • Extensibility: The array and the fallback request handler are intentionally exposed for quick modification, making it trivial to swap in custom tool handlers or integrate additional data sources.

Use cases

  • Rapid prototyping: Start a new AI project by spinning up this server, adding a few domain‑specific tools, and having Claude interact with them instantly.
  • Internal tooling: Expose company APIs (e.g., HR, inventory, ticketing) to an AI assistant so that employees can query or update information conversationally.
  • Educational demos: Show students how MCP servers work by extending the template with simple arithmetic or data‑retrieval tools.
  • Integration testing: Use the server as a mock MCP endpoint to validate Claude Desktop configurations or to benchmark tool performance.

Integration with AI workflows

Once the server is running, Claude Desktop automatically discovers it via its configuration file. The assistant can then list available tools in the chat UI, invoke them with natural language prompts (e.g., ), and receive structured responses that the assistant can embed in its replies. Because the server adheres to the official MCP specification, any downstream tooling—logging, monitoring, or security layers—can interact with it using standard protocols without needing custom adapters.

Unique advantages

  • Zero configuration beyond the template: The server requires only Node.js and the SDK; no additional infrastructure or complex deployment steps.
  • Clear separation of concerns: Protocol handling lives in the SDK, while business logic is isolated in tool handlers, making maintenance straightforward.
  • Community‑ready: Built under an MIT license and designed to be forked, the template invites collaboration and rapid iteration across teams.

In short, the MCP JS Server Template is a practical foundation that accelerates the creation of reliable, protocol‑compliant AI assistants. By abstracting away the low‑level details and providing a clean, extensible structure, it empowers developers to bring custom tools and data sources into the conversational loop with minimal friction.