agent-todo-mcp

agent-todo-mcp

A Model Context Protocol server for AI agents to manage tasks and track progress across projects, with features like project isolation, search, and reporting.

Category
Visit Server

README

Agent TODO MCP Server

Now all editors have this feature so yep...

A Model Context Protocol server for AI agents to manage tasks and track progress across projects.

Features

  • Create, update, and manage TODOs with detailed metadata
  • Track progress, status, priority, and dependencies
  • Explicit project isolation to prevent data mixup between workspaces
  • Search, filter, and generate comprehensive reports
  • Persistent JSON storage with complete project separation

Installation

Via npm (Recommended)

npm install -g agent-todo-mcp

From Source

git clone https://github.com/w04m1/agent-todo-mcp.git
cd agent-todo-mcp
npm install
npm run build
npm install -g .

Configuration

Add to your Claude Desktop/Cursor/VSCode/etc. config:

{
  "mcpServers": {
    "agent-todo": {
      "command": "agent-todo-mcp"
    }
  }
}

How AI Models Use This Server

When AI models interact with this MCP server, they follow this workflow:

  1. Check existing projects with list_projects
  2. Create or switch to a project with switch_project
  3. Create and manage TODOs within that project workspace

Project Management

Project Naming Best Practices

When creating projects, use descriptive names that clearly identify the workspace:

  • ✅ "my-react-app" - Good descriptive name
  • ✅ "backend-api-v2" - Clear project identifier
  • ✅ "research-ml-models" - Descriptive and specific
  • ❌ "project1" - Too generic
  • ❌ "temp" - Not descriptive

Project Isolation & Storage

Each project workspace is completely isolated. TODOs are stored in:

~/.agent-todos/
├── my-react-app/todos.json      # Project: "my-react-app"
├── backend-api-v2/todos.json    # Project: "backend-api-v2"
├── research-ml-models/todos.json # Project: "research-ml-models"
└── default-workspace/todos.json # Default fallback project

Architecture

  • Complete Isolation: Each project has its own TODO storage
  • Explicit Management: Projects are created explicitly via switch_project tool
  • Persistent Storage: All data persists in ~/.agent-todos/{projectId}/
  • ⚠️ No Deletion: Projects cannot be deleted through the API (only individual TODOs can be deleted)

Available Tools

Project Management

  • list_projects - List all available project workspaces
  • switch_project - Create new project or switch between existing ones
  • get_project_info - Show current project details

Core Management

  • create_todo - Create new tasks
  • update_todo - Update existing tasks
  • delete_todo - Remove tasks
  • list_todos - List and filter tasks
  • get_todo - Get detailed task info

Search & Analytics

  • search_todos - Search across all tasks
  • generate_report - Create progress reports
  • get_stats - Quick statistics

TODO Structure

interface Todo {
  id: string;
  title: string;
  description?: string;
  status: "pending" | "in-progress" | "completed" | "blocked";
  priority: "low" | "medium" | "high" | "urgent";
  progress: number; // 0-100
  tags: string[];
  dependencies: string[]; // Other TODO IDs
  dueDate?: string;
  metadata: Record<string, any>;
  createdAt: string;
  updatedAt: string;
}

Development

npm run dev    # Development mode
npm run build  # Build project
npm start      # Run built server

License

MIT


Built with AI for AI 🤡

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured