AI Collaboration MCP Server

🚧 Work in Progress - Active Development 🚧

A Model Context Protocol (MCP) server designed to facilitate direct AI-to-AI collaboration between Claude and Gemini, eliminating the need for human intermediation in development workflows.

Note: This project is under active development. While core features are functional, some aspects are still being refined. Contributions and feedback are welcome!

🎯 Project Goal

Enable truly autonomous AI-to-AI collaboration where:

AI agents work continuously on complex projects
Human intervention is minimal (ideally just starting the process)
Agents create comprehensive project plans and execute 100+ phases autonomously
Work continues until project completion or critical blocker

🚀 Quick Start

Both AIs just run:

@ai-collab init {"agentName": "gemini", "autonomous": true}  // For Gemini (CTO)
@ai-collab init {"agentName": "claude", "autonomous": true}   // For Claude (Developer)

That's it! The init command:

Loads existing project context and state
Creates or resumes a comprehensive project plan
Automatically detects and continues pending work
Shows critical tickets and blockers
Starts autonomous execution loops

🌟 Recent Enhancements

Workflow Optimization (v2.0) 🚀

Task Dependencies: Define dependsOn relationships between tasks
Batch Task Creation: CTO can create multiple tasks in one command
Priority-Based Work: Tasks are automatically prioritized (high/medium/low)
Continuous Developer Mode: No waiting between tasks - automatic progression
Smart Task Status: available, blocked, in_progress, in_review, completed
Dependency Resolution: Tasks automatically unblock when dependencies complete

Autonomous Loop System

120-second check intervals for more natural workflow pacing
500 iteration maximum for extended autonomous operation
Continuous work mode - agents keep working until project completion
Manual loop execution - requires human to run check commands (automation WIP)

Project Plan Management

Auto-generated 6-phase plans from PROJECT_REQUIREMENTS.md
Smart phase progression - automatically moves to next phase when complete
Duplicate task detection - prevents recreating completed features
Ad-hoc mission support - pause main plan for urgent tasks

Enhanced Validation

Ticket vs Task distinction - prevents confusion between bug reports and work items
Role-based instructions - clearer guidance for CTO vs Developer roles
Workflow enforcement - ensures proper task creation and submission flow

⚠️ Current Limitations

Automation Challenges

Manual loop execution required - AI agents can't schedule their own checks
PATH configuration needed - Claude/Gemini commands must be accessible
API quota limits - Gemini has daily request limits that may be exceeded

Addressed Issues ✅

~~Single task queuing~~ → Now supports batch task creation
~~Developer idle time~~ → Continuous work mode implemented
~~No task dependencies~~ → Full dependency system added
~~Random task order~~ → Priority-based scheduling active

Remaining Challenges

Agents occasionally create duplicate tasks (improved but not eliminated)
Edit button functionality may need manual verification
Loop execution still requires human intervention

Workarounds Available

Automation scripts provided (mcp-automator.js) but require setup
Manual loop execution instructions included
Simulation mode for tracking when automation fails

Features

Core Capabilities

Comprehensive Project Plans: 100+ phase autonomous execution capability
One-Command Startup: Just init with autonomous flag
Role-Based System: CTO, Developer, PM, QA, Architect roles
Smart Task Management: Duplicate detection and phase progression
Ticketing System: Track bugs, enhancements, tech debt
Context Retention: Maintains state across sessions
Mission Management: High-level objectives with auto-decomposition
Code Review Workflow: Submit, review, and revision cycles
Question & Answer System: Asynchronous clarifications
Comprehensive Logging: Full audit trail

🆕 Enhanced Workflow Features (v2.0)

Task Dependencies: Tasks can depend on other tasks with automatic blocking/unblocking
Priority-Based Scheduling: High/medium/low priority with smart task selection
Batch Task Creation: CTO can queue multiple tasks at once for efficiency
Continuous Work Mode: Developer automatically moves to next available task
Smart Status System: available, blocked, in_progress, in_review, completed
Dependency Visualization: Clear indication of task dependencies and blockers

Installation

Clone this repository:

git clone https://github.com/yourusername/ai-collab-mcp.git
cd ai-collab-mcp

Install dependencies:

npm install

Make the server executable:

chmod +x src/index.js

Configuration

For Claude Code

Create a .mcp.json file in your project root:

{
  "mcpServers": {
    "ai-collab": {
      "command": "node",
      "args": [".mcp-server/src/index.js"],
      "cwd": "/path/to/your/project"
    }
  }
}

For Gemini

Configure in ~/.gemini/settings.json:

{
  "mcpServers": {
    "ai-collab": {
      "command": "node",
      "args": ["/path/to/ai-collab-mcp/src/index.js"]
    }
  }
}

Note: Gemini may require explicit instructions to execute MCP commands.

Usage

🎯 Autonomous Mode (Recommended)

Start with autonomous flag for continuous operation:

# Terminal 1 - Claude (Developer)
@ai-collab init {"agentName": "claude", "autonomous": true}

# Terminal 2 - Gemini (CTO)  
@ai-collab init {"agentName": "gemini", "autonomous": true}

# Terminal 3 - Manual Loop Execution (Required)
# Every 120 seconds, run:
@ai-collab get_loop_status {"agentName": "claude"}
@ai-collab get_loop_status {"agentName": "gemini"}

Automation Helpers (Experimental)

For reduced manual intervention:

# Run automation script (requires setup)
cd /path/to/project
node mcp-automator.js auto

# Or simulation mode (shows what would happen)
node mcp-automator-v2.js auto

See AUTOMATION.md for setup details.

Traditional Commands

CTO Tools

send_directive - Create development tasks (now with dependencies & priority)
send_batch_directives - Create multiple tasks at once
review_work - Review submissions
create_project_plan - Start comprehensive plan
update_plan_progress - Move to next phase

Developer Tools

get_all_tasks - View assigned work (sorted by priority)
submit_work - Submit completed tasks
ask_question - Request clarification

🆕 Enhanced Workflow Examples

Creating Tasks with Dependencies

// Single task with dependency
@ai-collab send_directive {
  "taskId": "KAN-002",
  "title": "Create database tables",
  "specification": "Create user and project tables",
  "priority": "high",
  "dependsOn": ["KAN-001"]  // Won't be available until KAN-001 is approved
}

// Batch creation with dependencies
@ai-collab send_batch_directives {
  "tasks": [
    {
      "taskId": "KAN-003",
      "title": "Setup database connection",
      "specification": "Configure PostgreSQL connection",
      "priority": "high"
    },
    {
      "taskId": "KAN-004", 
      "title": "Create user model",
      "specification": "Implement User model with validation",
      "priority": "medium",
      "dependsOn": ["KAN-003"]
    },
    {
      "taskId": "KAN-005",
      "title": "Create auth endpoints",
      "specification": "Implement login/register endpoints",
      "priority": "medium",
      "dependsOn": ["KAN-004"]
    }
  ]
}

Continuous Work Mode (Developer)

When the developer runs get_loop_status, they will:

See prioritized available tasks
Automatically start on the highest priority task
After submitting, immediately move to next task
Continue until all available tasks are complete

No more waiting between tasks! The developer keeps working continuously.

Project Plan Workflow

Automatic Plan Creation: On first init, generates 6-phase plan from requirements
Phase Progression: Automatically advances when all phase tasks complete
Duplicate Prevention: Skips tasks that match completed work
Ad-hoc Missions: Can pause main plan for urgent work

Example phases:

Foundation & Basic Structure
Core Interactive Features
UI/UX Enhancement
Data Persistence
Advanced Features
Polish & Quality Assurance

Data Storage

data/
├── tasks.json              # Task tracking
├── missions.json           # Active missions
├── project-state.json      # Project configuration
├── project-plans.json      # Comprehensive plans (NEW)
├── loop-states.json        # Autonomous loop tracking (NEW)
└── tickets/
    └── tickets.json        # Bug/enhancement tracking

Troubleshooting

Gemini Not Executing Commands

Prefix with: "Execute the following MCP command:"
Or: "Use the ai-collab tool to run:"

Duplicate Task Creation

System now detects similar task names
Manually clean duplicates from data/tasks.json if needed

Loop Not Continuing

Ensure 120-second intervals between checks
Verify agent hasn't exceeded maxIterations (500)
Check API quotas haven't been exceeded

Contributing

This project needs help with:

True automation (removing manual loop execution)
Better Gemini CLI integration
Improved duplicate detection algorithms
Cross-platform automation scripts

Fork the repository
Create feature branch (git checkout -b feature/improvement)
Commit changes (git commit -m 'Add improvement')
Push branch (git push origin feature/improvement)
Open Pull Request

Roadmap

License

MIT License - see LICENSE file for details.

Support

For issues, questions, or contributions, please open an issue on GitHub.

Remember: This is an experimental project pushing the boundaries of AI collaboration. Expect rough edges but exciting possibilities!