AI Collaboration MCP Server

Local setup required. This server has to be cloned and prepared on your machine before you register it in Claude Code.
1

Set the server up locally

Run this once to clone and prepare the server before adding it to Claude Code.

Run in terminal
git clone https://github.com/wyn0001/ai-collab-mcp.git
cd ai-collab-mcp
npm install
2

Register it in Claude Code

After the local setup is done, run this command to point Claude Code at the built server.

Run in terminal
claude mcp add ai-collab -- node "<FULL_PATH_TO_AI_COLLAB_MCP>/dist/index.js"

Replace <FULL_PATH_TO_AI_COLLAB_MCP>/dist/index.js with the actual folder you prepared in step 1.

README.md

Facilitate direct AI-to-AI collaboration between Claude and Gemini

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

  1. Clone this repository:
git clone https://github.com/yourusername/ai-collab-mcp.git
cd ai-collab-mcp
  1. Instal

Tools (1)

initInitializes the AI collaboration session, loads project context, and starts execution loops.

Configuration

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

Try it

→Initialize the collaboration session as the developer agent in autonomous mode.
→Check the current status of the project plan and identify any pending tasks.
→Create a new task for the CTO agent to review the current architecture.
→List all current blockers preventing the completion of the project phases.

Frequently Asked Questions

What are the key features of AI Collaboration MCP Server?

Autonomous AI-to-AI collaboration between Claude and Gemini. Role-based system including CTO, Developer, PM, QA, and Architect. Task dependency management with automatic unblocking. Continuous execution loops for complex multi-phase projects. Smart task management with duplicate detection and priority scheduling.

What can I use AI Collaboration MCP Server for?

Automating long-running software development projects without constant human oversight. Coordinating complex tasks between different AI models to leverage their specific strengths. Managing large-scale project plans with hundreds of phases and dependencies. Maintaining state and context across multiple AI-driven development sessions.

How do I install AI Collaboration MCP Server?

Install AI Collaboration MCP Server by running: git clone https://github.com/wyn0001/ai-collab-mcp.git && cd ai-collab-mcp && npm install

What MCP clients work with AI Collaboration MCP Server?

AI Collaboration MCP Server works with any MCP-compatible client including Claude Desktop, Claude Code, Cursor, and other editors with MCP support.

Turn this server into reusable context

Keep AI Collaboration MCP Server docs, env vars, and workflow notes in Conare so your agent carries them across sessions.

Need the old visual installer? Open Conare IDE.
Open Conare
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