Welcome to Aidderall MCP Server!
Aidderal is a Model Context Protocol (MCP) server implementation for hierarchical task management, providing AI assistants with a cognitive prosthetic for maintaining focus and context across complex problem-solving sessions.
AI assistants have a hard time dealing with long running tasks, in part due to limitations in the amount of information they can handle at any one point in time (context window).
Aidderall solves this issue by giving the AI assistant:
Focus
A map of the current cognitive landscape with context
Think of aidderall as many small context windows which help the AI focus and remember!
Overview
Aidderall implements a flexible hierarchical task management system where:
Tasks can be organized in nested structures for complex work decomposition
Create independent tasks for parallel work streams
Navigate freely between any tasks using switch_focus
Complete tasks in any order that makes sense for your workflow
All tasks remain visible as a living document of your work
What does Aidderall look like?
Features
Hierarchical Task Management: Create main tasks and extend them with subtasks
Focus Enforcement: Only one task can be active at a time
Context Preservation: Maintain breadcrumb trails and sibling awareness
Task Completion: Archive completed tasks with timestamps
State Persistence: Track all tasks and their relationships
Acknowledgements
Thanks to Alberto for coming up with the name... the project name was not so exciting before! 😀
Installation
Clone the repository:
Create and activate a virtual environment:
Install dependencies:
For development, install with dev dependencies:
Usage
Running the Server
Start the MCP server:
The server runs via the Python module directly.
Integrating with AI Assistants
For Claude Code
If you have Claude Code installed, you can add Aidderall directly:
Manual Configuration
Alternatively, add the server to your MCP configuration:
Available Commands
Task Creation
create_new_task(title, body)
- Create a new independent task for unrelated work or new topicsextend_current_task(title, body)
- Add a subtask to organize and break down the current task
Navigation & Information
get_current_task()
- Returns current task with contextget_big_picture(format='text')
- Shows entire task hierarchy (format: 'text' or 'json')get_stack_overview()
- Returns JSON structure of systempeek_context(include_body=False)
- View parent and sibling contextlist_siblings(include_body=False)
- List tasks at current level
Task Management
complete_current_task()
- Mark current task as complete (stays visible)update_current_task(body)
- Update current task bodyswitch_focus(task_id)
- Switch focus to any task by IDremove_task(task_id)
- Remove task from structure (preserves in history)get_completed_tasks(order)
- View completed task archive
Example Workflow
Development
Running Tests
Code Coverage
Code Formatting
Type Checking
Architecture
models.py
- Core data structures (Task, MainTask, SubTask)task_manager.py
- Task management logic and state handlinghandlers.py
- MCP command implementationsserver.py
- MCP server entry point
Documentation
Usage Guide - Comprehensive guide for getting AI assistants to use Aidderall effectively
Technical Specification - Detailed technical specification of the hybrid stack-list model
AI Assistant Evolution - Vision for transforming AI from stateless oracle to focused worker
Work Log - Development history and architectural decisions
Contributing
Fork the repository
Create a feature branch
Add tests for new functionality
Ensure all tests pass
Submit a pull request
License
This project is licensed under the GNU General Public License v3.0 or later - see the LICENSE file for details.
Copyright (C) 2024 Briam R. briamr@gmail.com
Tools
Provides AI assistants with a hierarchical task management system that maintains focus and context across complex problem-solving sessions, solving context window limitations through organized task structures.
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