CoordMCP
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@followed by the MCP server name and your instructions, e.g., "@CoordMCPstart a new project with Python and React"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CoordMCP - Multi-Agent Code Coordination Server
CoordMCP is a coordination server that helps multiple AI coding agents work together on the same project without conflicts.
Why CoordMCP?
When you use AI coding assistants (OpenCode, Cursor, Claude Code, Windsurf) on a project:
Lost decisions - The AI forgets what was decided in previous sessions
Inconsistent choices - Different sessions make different architectural decisions
No coordination - Multiple AI agents don't know what each other is doing
No history - There's no record of why certain decisions were made
CoordMCP solves this by giving your AI agents a shared brain that persists across sessions.
How It Works
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ YOU │────▶│ AI AGENT │────▶│ CoordMCP │
│ │ │ │ │ Server │
└─────────────┘ └─────────────┘ └──────┬──────┘
│
▼
┌─────────────────┐
│ Shared Memory │
│ • Decisions │
│ • Tech Stack │
│ • File Locks │
└─────────────────┘You just talk to your AI agent normally. CoordMCP works automatically in the background:
Remembers decisions across sessions
Prevents file conflicts between agents
Provides architecture recommendations
Tracks all changes
Example
You say:
"Create a todo app with React and FastAPI"
CoordMCP automatically:
Discovers or creates the project
Registers your AI agent
Locks files before editing
Records "Use React" and "Use FastAPI" decisions
Tracks all created/modified files
Unlocks files when done
Next session: Your AI remembers you're using React and FastAPI.
Quick Start
Install
pip install coordmcp
coordmcp --versionConfigure Your Agent
Option 1: Using coordmcp CLI (recommended)
For most agents, add to your config file:
{
"mcpServers": {
"coordmcp": {
"command": "coordmcp",
"args": [],
"env": {
"COORDMCP_LOG_LEVEL": "INFO"
}
}
}
}Option 2: Using Python module
{
"mcpServers": {
"coordmcp": {
"command": "python",
"args": ["-m", "coordmcp"],
"env": {
"COORDMCP_LOG_LEVEL": "INFO"
}
}
}
}See integrations for specific setup instructions for each agent.
Test It
Restart your AI agent and say:
"What CoordMCP tools are available?"
Documentation
Audience | Start Here |
End Users | |
Developers | |
Contributors | |
Architecture Decisions |
User Guide
What is CoordMCP? - Overview and features
Installation - Install and configure
How It Works - Behind the scenes
Integrations
Developer Guide
API Reference - All 52 tools
Data Models - Data structures
Examples - Usage examples
Contributor Guide
Architecture - System design
Development Setup - Dev environment
Testing - Run and write tests
Extending - Add new features
Reference
Troubleshooting - Common issues
Configuration - All options
Architecture Decision Records - Design decisions
Features
Long-Term Memory
Your AI agent remembers decisions across sessions. If you chose React last week, it knows this week.
Multi-Agent Coordination
Multiple AI agents can work on the same project without conflicts through file locking.
Architecture Guidance
Design pattern recommendations without expensive LLM calls. 9 patterns available: MVC, Repository, Service, Factory, Observer, Adapter, Strategy, Decorator, CRUD.
Task Management
Create, assign, and track tasks across agents. Support for task dependencies, priorities, and completion tracking.
Agent Messaging
Enable communication between agents with direct messages and broadcast capabilities.
Health Dashboard
Monitor project health with comprehensive dashboards showing task progress, agent activity, and actionable recommendations.
Zero LLM Costs
All architectural analysis is rule-based - no external API calls needed.
Development
git clone https://github.com/yourusername/coordmcp.git
cd coordmcp
pip install -e ".[dev]"
python -m pytest src/tests/ -vLicense
MIT License - see LICENSE.
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