About MCP Servers
A monorepo for developers building MCP servers, managed with pnpm workspaces and Turborepo. It currently includes a demo server that provides a simple addition tool, along with TypeScript, build, formatting, and linting support.
Key features
- 01Demo addition MCP server
- 02pnpm workspaces dependency management
- 03Turborepo builds
- 04TypeScript support
- 05Prettier formatting and ESLint linting
Use cases
- 01Developing an MCP server example
- 02Building multiple packages in a monorepo
MCP support
The repository includes an MCP server that implements a simple addition tool for MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Servers
This is a monorepo containing various Model Context Protocol (MCP) server implementations.
Packages
@mcp-servers/add-demo: A demo MCP server that implements a simple addition tool
Development
This project uses pnpm workspaces and Turborepo for fast, efficient builds. To get started:
- Install pnpm (if you haven't already):
npm install -g pnpm
- Install dependencies:
pnpm install
- Build all packages:
pnpm build
- Run development mode:
pnpm dev
- Run linting:
pnpm lint
- Format code:
pnpm format
Project Structure
mcp-servers/
├── packages/
│ └── add-demo/ # Demo MCP server with add tool
├── package.json # Root package.json with workspace config
├── pnpm-workspace.yaml # PNPM workspace configuration
├── turbo.json # Turborepo configuration
└── tsconfig.json # Base TypeScript configuration
Features
- 🚀 Fast builds with Turborepo
- 📦 Efficient dependency management with pnpm
- 🔍 TypeScript support
- 🧹 Code formatting with Prettier
- 🔍 Linting with ESLint
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.