MCP Task Manager

A Model Context Protocol (MCP) server for comprehensive task management, deployed as a Cloudflare Worker. This open-source project enables AI assistants to plan, track, and manage complex multi-step requests efficiently with persistent storage using Cloudflare KV.

🚀 Features

- Request Planning: Break down complex requests into manageable tasks
- Task Management: Create, update, delete, and track task progress
- Approval Workflow: Built-in approval system for task and request completion
- Progress Tracking: Visual progress tables and detailed task information
- Persistent Storage: Uses Cloudflare KV for reliable data persistence
- Serverless Architecture: Deployed as a Cloudflare Worker for global availability
- RESTful API: HTTP endpoints for easy integration with any application
- CORS Support: Cross-origin requests enabled for web applications

📦 Deployment

Prerequisites

- Cloudflare account (free tier works)
- Wrangler CLI installed
- Node.js 18+ and npm/pnpm/yarn
- Git for cloning the repository

Quick Start

1. Clone and setup the repository

   git clone https://github.com/Rudra-ravi/mcp-taskmanager.git

   cd mcp-taskmanager

   npm install

   

2. Login to Cloudflare

   npx wrangler login

   

This will open your browser to authenticate with Cloudflare.

3. Create KV namespace

   npx wrangler kv namespace create "TASKMANAGER_KV"

   

Copy the namespace ID from the output.

4. Update configuration
Edit wrangler.toml and replace the KV namespace ID:

   [[kv_namespaces]]

   binding = "TASKMANAGER_KV"

   id = "your-new-kv-namespace-id-here"

   

5. Build and deploy

   npm run build

   npx wrangler deploy

   

Your MCP Task Manager will be deployed and accessible at:
https://mcp-taskmanager.your-subdomain.workers.dev

Advanced Configuration

Custom Worker Name

To deploy with a custom name, update wrangler.toml:

name = "my-custom-taskmanager"  # Change this to your preferred name
main = "worker.ts"
compatibility_date = "2024-03-12"
[build]

command = "npm run build"
[[kv_namespaces]]

binding = "TASKMANAGER_KV"

id = "your-kv-namespace-id-here"

Environment Variables

For different environments (development, staging, production):

[env.staging]
name = "mcp-taskmanager-staging"
[[env.staging.kv_namespaces]]
binding = "TASKMANAGER_KV"
id = "staging-kv-namespace-id"
[env.production]

name = "mcp-taskmanager-prod"

[[env.production.kv_namespaces]]

binding = "TASKMANAGER_KV"

id = "production-kv-namespace-id"

Deploy to specific environments:

npx wrangler deploy --env staging

npx wrangler deploy --env production

🔧 Usage

API Endpoints

The deployed worker provides two main endpoints:

- POST /list-tools - Get available MCP tools
- POST /call-tool - Execute MCP tool functions

Testing Your Deployment

After deployment, test your worker with curl:

# Replace with your actual worker URL
WORKER_URL="https://mcp-taskmanager.your-subdomain.workers.dev"
Test list tools
curl -X POST $WORKER_URL/list-tools \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}'
Test creating a request
curl -X POST $WORKER_URL/call-tool \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "request_planning",
      "arguments": {
        "originalRequest": "Test deployment",
        "tasks": [{"title": "Test task", "description": "Verify deployment works"}]
      }
    }
  }'

Available Tools

📋 Core Task Management

- request_planning - Register a new user request and plan its associated tasks - get_next_task - Get the next pending task for a request - mark_task_done - Mark a task as completed with optional details - approve_task_completion - Approve a completed task - approve_request_completion - Approve the completion of an entire request

⚙️ Task Operations

- add_tasks_to_request - Add new tasks to an existing request - update_task - Update task title or description (only for pending tasks) - delete_task - Remove a task from a request - open_task_details - Get detailed information about a specific task

📊 Information & Monitoring

- list_requests - List all requests with their current status and progress

Example API Calls

List Available Tools

curl -X POST https://your-worker.your-subdomain.workers.dev/list-tools \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/list"
  }'

Plan a New Request

curl -X POST https://your-worker.your-subdomain.workers.dev/call-tool \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "request_planning",
      "arguments": {
        "originalRequest": "Build a web application for task management",
        "splitDetails": "Breaking down into frontend, backend, and deployment tasks",
        "tasks": [
          {
            "title": "Setup React frontend",
            "description": "Initialize React app with TypeScript and essential dependencies"
          },
          {
            "title": "Create backend API",
            "description": "Build REST API with Node.js and Express"
          },
          {
            "title": "Deploy application",
            "description": "Deploy to cloud platform with CI/CD pipeline"
          }
        ]
      }
    }
  }'

📊 Data Model

Task Structure

interface Task {
  id: string;              // Unique task identifier (e.g., "task-1")
  title: string;           // Task title
  description: string;     // Detailed task description
  done: boolean;           // Whether task is marked as done
  approved: boolean;       // Whether task completion is approved
  completedDetails: string; // Details provided when marking task as done
}

Request Structure

interface RequestEntry {
  requestId: string;       // Unique request identifier (e.g., "req-1")
  originalRequest: string; // Original user request description
  splitDetails: string;    // Details about how request was split into tasks
  tasks: Task[];          // Array of tasks for this request
  completed: boolean;     // Whether entire request is completed
}

Task Status Flow

❌ Pending → ⏳ Done (awaiting approval) → ✅ Approved

Tasks can only be updated when in "Pending" status. Once marked as done or approved, they become read-only.

🛠️ Development

Local Development

# Install dependencies
npm install
Build the project
npm run build
Start local development server (with remote KV)
npx wrangler dev
Start local development server (with local KV for testing)
npx wrangler dev --local
Deploy to preview environment
npx wrangler deploy --env preview

Testing

# Test the build
npm run build
Test deployment (dry run - shows what would be deployed)
npx wrangler deploy --dry-run
Run local tests
npm test  # If you add tests
Test with local KV storage
npx wrangler dev --local

Debugging

View real-time logs:

# Tail logs from deployed worker

npx wrangler tail

Tail logs with filtering
npx wrangler tail --format pretty

KV Data Management

# List all keys in your KV namespace
npx wrangler kv:key list --binding TASKMANAGER_KV
Get a specific key value
npx wrangler kv:key get "tasks" --binding TASKMANAGER_KV
Delete all data (be careful!)
npx wrangler kv:key delete "tasks" --binding TASKMANAGER_KV

🏗️ Architecture

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