Task Manager MCP Server

A small Model Context Protocol (MCP) server, built in Node.js, that lets an AI assistant like Claude manage a to-do list on your behalf.

It's intentionally minimal — meant as a clear, working example of how MCP tools and resources are wired up, not a production task manager.

What it does

The server exposes:

Tools (actions the model can call):

• add_task — add a new task with an optional priority

• list_tasks — list tasks, optionally filtered by open / done

• complete_task — mark a task as done by ID

• delete_task — remove a task by ID

Resources (data the model can read):

• tasks://all — the full task list as JSON

Tasks are persisted to a local tasks.json file, so they survive restarts.

Tech stack

• Node.js (ES modules)

• @modelcontextprotocol/sdk — official MCP SDK

• zod — schema validation for tool inputs

• Transport: stdio (designed to be launched by an MCP host, not run standalone in a browser)

Setup

git clone https://github.com/<your-username>/task-manager-mcp.git
cd task-manager-mcp
npm install

Running it

MCP servers communicate over stdio and are meant to be launched by a host application (like Claude Desktop), not opened directly in a browser.

Connect it to Claude Desktop

Add this to your Claude Desktop config file (%APPDATA%\Claude\claude_desktop_config.json on Windows, or ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "task-manager": {
      "command": "node",
      "args": ["/absolute/path/to/task-manager-mcp/index.js"]
    }
  }
}

Restart Claude Desktop, and you'll see the tools show up — you can then just ask Claude things like:

"Add a task to finish the quarterly report, high priority" "What tasks do I still have open?" "Mark task 2 as done"

Project structure

task-manager-mcp/
├── index.js        # The MCP server: tools, resources, and the stdio transport
├── package.json
├── .gitignore
└── README.md

Why this is a useful showcase

This project demonstrates the two core building blocks of MCP:

• Tools, which let a model take actions with side effects (here: mutating a task list)

• Resources, which let a model read structured data directly (here: the current task list as JSON)

It's a good starting point to fork and adapt — swap the JSON file for a real database, add more tools, or extend it with prompts.

License

MIT