@astrotask/mcp

@astrotask/mcp

MCP (Model Context Protocol) server for Astrotask task management.

Overview

The @astrotask/mcp package implements a Model Context Protocol server that exposes Astrotask's task management capabilities to AI agents. It provides a set of well-defined tools that agents can use to create, update, query, and manage tasks in a structured and type-safe manner.

Features

• 🤖 AI Agent Integration: Full MCP compatibility for seamless AI agent interaction
• 🛠️ Rich Tool Set: Comprehensive tools for task lifecycle management
• 🔒 Type Safety: Input validation and type checking for all operations
• 📊 Context-Aware: Intelligent task context bundling for agents
• 🔄 Real-time Operations: Live task management with immediate feedback
• 📋 Hierarchical Support: Full support for nested tasks and complex relationships

Installation & Usage

Using npx (Recommended)

Run the MCP server directly without installation:

npx @astrotask/mcp

Global Installation

npm install -g @astrotask/mcp
astrotask-mcp

Configuration

The MCP server uses environment variables for configuration:

# Database configuration
DATABASE_URI=sqlite://./data/astrotask.db

# Optional: Enable verbose database logging
DB_VERBOSE=true

# Run the server
npx @astrotask/mcp

MCP Tools

The server provides 6 essential tools for AI agent task management:

• getNextTask - Get the next available task to work on
• addTasks - Create multiple tasks with hierarchies and dependencies
• listTasks - List tasks with optional filtering
• addTaskContext - Add context information to tasks
• addDependency - Create dependency relationships between tasks
• updateStatus - Update task status (pending, in-progress, done, etc.)

Integration with AI Agents

Configure your AI agent (Cursor, ChatGPT, etc.) to use this MCP server:

{
  "name": "getNextTask",
  "arguments": { "priority": "high" }
}

Available Tools

The MCP server exposes the following tools for AI agents:

Task Management

listTasks

List tasks with optional filtering.

Parameters:

• status (optional): Filter by task status (pending, in-progress, done, cancelled)
• parentId (optional): Filter by parent task ID
• includeSubtasks (optional): Include nested subtasks in results

Example:

{
  "name": "listTasks",
  "arguments": {
    "status": "pending",
    "includeSubtasks": true
  }
}

createTask

Create a new task.

Parameters:

• title (required): Task title
• description (optional): Detailed task description
• status (optional): Initial status (default: pending)
• parentId (optional): Parent task for subtasks
• prd (optional): Product Requirements Document content
• contextDigest (optional): Context digest for AI agents

Example:

{
  "name": "createTask",
  "arguments": {
    "title": "Implement user authentication",
    "description": "Add JWT-based authentication with refresh token support",
    "status": "pending"
  }
}

updateTask

Update an existing task.

Parameters:

• id (required): Task ID to update
• title (optional): New task title
• description (optional): New task description
• status (optional): New task status
• parentId (optional): New parent task ID
• prd (optional): New PRD content
• contextDigest (optional): New context digest

Example:

{
  "name": "updateTask",
  "arguments": {
    "id": "A",
    "status": "done",
    "description": "Completed: Added JWT auth with bcrypt password hashing"
  }
}

deleteTask

Delete a task.

Parameters:

• id (required): Task ID to delete
• cascade (optional): Whether to delete all subtasks (default: false)

Example:

{
  "name": "deleteTask",
  "arguments": {
    "id": "A",
    "cascade": true
  }
}

completeTask

Mark a task as complete. This is a convenience tool that sets status to done.

Parameters:

• id (required): Task ID to complete

Example:

{
  "name": "completeTask",
  "arguments": {
    "id": "A"
  }
}

getTaskContext

Retrieve a task with its full context including related tasks.

Parameters:

• id (required): Task ID to get context for
• includeAncestors (optional): Include parent tasks (default: false)
• includeDescendants (optional): Include child tasks (default: false)
• maxDepth (optional): Maximum depth for hierarchical inclusion (default: 3)

Example:

{
  "name": "getTaskContext",
  "arguments": {
    "id": "A",
    "includeAncestors": true,
    "includeDescendants": true,
    "maxDepth": 5
  }
}

Configuration

Environment Variables

Configure the MCP server through environment variables:

# Database configuration
DATABASE_URI=./tasks.db
DATABASE_ENCRYPTED=true
DATABASE_KEY=your-encryption-key

# Logging configuration
LOG_LEVEL=info
NODE_ENV=production

# Server configuration
MCP_SERVER_NAME=astrotask
MCP_SERVER_VERSION=1.0.0

Command Line Options

npx @astrotask/mcp --help

Options:
  --database-path <path>    Database file path (default: ./tasks.db)
  --encrypted              Enable database encryption
  --log-level <level>      Log level (debug|info|warn|error)
  --help                   Show help information
  --version                Show version information

Integration Examples

Cursor IDE Integration

Configure Cursor to use Astrotask MCP server:

• Create .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "astrotask": {
      "command": "npx",
      "args": ["@astrotask/mcp"],
      "env": {
        "DATABASE_URI": "./tasks.db",
        "LOG_LEVEL": "info"
      }
    }
  }
}

• Restart Cursor and the MCP server will be available for AI interactions.

Claude Desktop Integration

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "astrotask": {
      "command": "npx",
      "args": ["@astrotask/mcp"],
      "env": {
        "DATABASE_URI": "/path/to/your/tasks.db"
      }
    }
  }
}

Custom AI Agent Integration

# Python example using MCP client
import asyncio
from mcp import Client

async def interact_with_astrotask():
    client = Client("npx @astrotask/mcp")
    
    # Create a new task
    result = await client.call_tool("createTask", {
        "title": "Review pull request #123",
        "description": "Review the authentication changes",
        "status": "pending"
    })
    
    task_id = result["id"]
    
    # Mark task as complete
    await client.call_tool("completeTask", {
        "id": task_id
    })
    
    # List all completed tasks
    completed = await client.call_tool("listTasks", {
        "status": "done"
    })
    
    print(f"Completed {len(completed)} tasks")

asyncio.run(interact_with_astrotask())

Error Handling

The MCP server provides structured error responses:

{
  "error": {
    "code": "INVALID_PARAMS",
    "message": "Task title is required",
    "data": {
      "field": "title",
      "receivedValue": null
    }
  }
}

Common error codes:

• INVALID_PARAMS: Invalid parameters provided
• NOT_FOUND: Requested task/resource not found
• VALIDATION_ERROR: Data validation failed
• DATABASE_ERROR: Database operation failed
• INTERNAL_ERROR: Unexpected server error

Performance Considerations

• Connection Pooling: The server reuses database connections efficiently
• Query Optimization: Database queries are optimized for common patterns
• Memory Management: Context retrieval limits depth to prevent memory issues
• Concurrent Operations: Multiple agents can safely operate simultaneously

Security

• Input Validation: All inputs are validated using Zod schemas
• SQL Injection Protection: All database queries use parameterized statements
• Encryption Support: Optional database encryption for sensitive data
• Access Control: Task access is scoped to the configured database

Development

Running in Development

# Clone the repository
git clone <repository-url>
cd astrotask

# Install dependencies
pnpm install

# Start development server with hot reload
pnpm --filter @astrotask/mcp dev

# Run tests
pnpm --filter @astrotask/mcp test

Custom Tool Development

Extend the MCP server with custom tools:

import { MCPHandler, HandlerContext } from '@astrotask/mcp';

class CustomTaskHandler implements MCPHandler {
  constructor(public readonly context: HandlerContext) {}

  async handleCustomOperation(params: CustomParams) {
    // Your custom logic here
    const result = await this.context.taskService.customOperation(params);
    return result;
  }
}

// Register the custom handler
server.addTool('customTask', CustomTaskHandler);

Debugging

Enable debug logging to troubleshoot issues:

# Enable debug logs
DEBUG=astrotask:* npx @astrotask/mcp

# Or set log level
LOG_LEVEL=debug npx @astrotask/mcp

The debug output includes:

• Incoming MCP requests and responses
• Database query execution
• Task service operations
• Error stack traces

Contributing

• Fork the repository
• Create a feature branch
• Implement your changes with tests
• Ensure all quality checks pass: pnpm verify
• Submit a pull request

License

MIT License - see LICENSE for details.

Related Packages

• @astrotask/core - Core task management library
• @astrotask/cli - Command-line interface

Support

• GitHub Issues - Bug reports and feature requests
• Documentation - Comprehensive guides and API reference
• Examples - Usage examples and tutorials

Enhanced MCP Tool Documentation

This MCP server uses an enhanced documentation approach that provides both brief descriptions and comprehensive documentation for each tool. Instead of the simple one-liner schema registration, we use the object form that supports:

Documentation Structure

Each MCP tool is registered with:

• description: One-sentence summary that appears in tooltips and quick help
• docs: Full Markdown documentation loaded from external files
• parameters: Complete Zod schema for argument validation

Documentation Files

The docs/ directory contains comprehensive Markdown documentation for each tool:

• docs/getNextTask.md - Get next available task with filtering options
• docs/addTasks.md - Batch task creation with hierarchies and dependencies
• docs/listTasks.md - Query tasks with various filters
• docs/addTaskContext.md - Add context slices to existing tasks
• docs/addDependency.md - Create task dependency relationships

Implementation Pattern

server.tool('toolName', {
  description: 'Brief one-sentence description for tooltips',
  docs: docsMarkdown,              // Full documentation from .md file
  parameters: zodSchema            // Complete Zod schema for validation
}, wrapMCPHandler(async (args) => {
  const parsedArgs = zodSchema.parse(args);  // Parse and validate
  return handlers.toolMethod(parsedArgs);
}));

Benefits

• Rich documentation: LLMs and IDEs can access comprehensive usage examples
• Type safety: Zod schemas provide runtime validation and TypeScript types
• Maintainable: Documentation is separated into focused, version-controlled files
• Consistent: All tools follow the same documentation pattern

This approach ensures that AI agents have access to both quick summaries and detailed usage information, improving their ability to effectively use the MCP tools.