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A2AMCP

by webdevtodayjason
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MIT License
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A2AMCP - Agent-to-Agent Model Context Protocol

License: MIT Docker Redis Status

Enabling Seamless Multi-Agent Collaboration for AI-Powered Development

A2AMCP brings Google's Agent-to-Agent (A2A) communication concepts to the Model Context Protocol (MCP) ecosystem, enabling AI agents to communicate, coordinate, and collaborate in real-time while working on parallel development tasks.

Originally created for SplitMind, A2AMCP solves the critical problem of isolated AI agents working on the same codebase without awareness of each other's changes.

✅ Server Status: WORKING! All 17 tools implemented and tested. Uses modern MCP SDK 1.9.3.

🚀 Quick Start

Using Docker (Recommended)

# Clone the repository git clone https://github.com/webdevtodayjason/A2AMCP cd A2AMCP # Start the server docker-compose up -d # Verify it's running docker ps | grep splitmind # Test the connection python verify_mcp.py

Configure Your Agents

Claude Code (CLI)

# Add the MCP server using Claude Code CLI claude mcp add splitmind-a2amcp \ -e REDIS_URL=redis://localhost:6379 \ -- docker exec -i splitmind-mcp-server python /app/mcp-server-redis.py

Claude Desktop

Add to your configuration file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{ "mcpServers": { "splitmind-a2amcp": { "command": "docker", "args": ["exec", "-i", "splitmind-mcp-server", "python", "/app/mcp-server-redis.py"], "env": { "REDIS_URL": "redis://redis:6379" } } } }

🎯 What Problem Does A2AMCP Solve?

When multiple AI agents work on the same codebase:

  • Without A2AMCP: Agents create conflicting code, duplicate efforts, and cause merge conflicts

  • With A2AMCP: Agents coordinate, share interfaces, prevent conflicts, and work as a team

Generic Use Cases Beyond SplitMind

A2AMCP can coordinate any multi-agent scenario:

  • Microservices: Different agents building separate services

  • Full-Stack Apps: Frontend and backend agents collaborating

  • Documentation: Multiple agents creating interconnected docs

  • Testing: Test writers coordinating with feature developers

  • Refactoring: Agents working on different modules simultaneously

🏗️ Architecture

┌─────────────────┐ │ A2AMCP Server │ ← Persistent Redis-backed MCP server │ (Port 5050) │ handling all agent communication └────────┬────────┘ │ STDIO Protocol (MCP) ┌────┴────┬─────────┬─────────┐ ▼ ▼ ▼ ▼ ┌────────┐┌────────┐┌────────┐┌────────┐ │Agent 1 ││Agent 2 ││Agent 3 ││Agent N │ │Auth ││Profile ││API ││Frontend│ └────────┘└────────┘└────────┘└────────┘

🔧 Core Features

1. Real-time Agent Communication

  • Direct queries between agents

  • Broadcast messaging

  • Async message queues

2. File Conflict Prevention

  • Automatic file locking

  • Conflict detection

  • Negotiation strategies

3. Shared Context Management

  • Interface/type registry

  • API contract sharing

  • Dependency tracking

4. Task Transparency

  • Todo list management

  • Progress visibility

  • Completion tracking

  • Task completion signaling

5. Multi-Project Support

  • Isolated project namespaces

  • Redis-backed persistence

  • Automatic cleanup

6. Modern MCP Integration

  • Uses MCP SDK 1.9.3 with proper decorators

  • @server.list_tools() and @server.call_tool() patterns

  • STDIO-based communication protocol

  • Full A2AMCP API compliance with 17 tools implemented

📦 Installation Options

Docker Compose (Production)

services: mcp-server: build: . container_name: splitmind-mcp-server ports: - "5050:5000" # Changed from 5000 to avoid conflicts environment: - REDIS_URL=redis://redis:6379 - LOG_LEVEL=INFO depends_on: redis: condition: service_healthy restart: unless-stopped redis: image: redis:7-alpine container_name: splitmind-redis ports: - "6379:6379" volumes: - redis-data:/data healthcheck: test: ["CMD", "redis-cli", "ping"] interval: 10s timeout: 5s retries: 5 volumes: redis-data: driver: local

Python SDK

pip install a2amcp-sdk

JavaScript/TypeScript SDK (Coming Soon)

npm install @a2amcp/sdk

🚦 Usage Example

Python SDK

from a2amcp import A2AMCPClient, Project, Agent async def run_agent(): client = A2AMCPClient("localhost:5000") project = Project(client, "my-app") async with Agent(project, "001", "feature/auth", "Build authentication") as agent: # Agent automatically registers and maintains heartbeat # Coordinate file access async with agent.files.coordinate("src/models/user.ts") as file: # File is locked, safe to modify pass # File automatically released # Share interfaces await project.interfaces.register( agent.session_name, "User", "interface User { id: string; email: string; }" )

Direct MCP Tool Usage

# Register agent register_agent("my-project", "task-001", "001", "feature/auth", "Building authentication") # Query another agent query_agent("my-project", "task-001", "task-002", "interface", "What's the User schema?") # Share interface register_interface("my-project", "task-001", "User", "interface User {...}")

📚 Documentation

🛠️ SDKs and Tools

Available Now

  • Python SDK: Full-featured SDK with async support

  • Docker Deployment: Production-ready containers

In Development

  • JavaScript/TypeScript SDK: For Node.js and browser

  • CLI Tools: Command-line interface for monitoring

  • Go SDK: High-performance orchestration

  • Testing Framework: Mock servers and test utilities

See SDK Development Progress for details.

🤝 Integration with AI Frameworks

A2AMCP is designed to work with:

  • SplitMind - Original use case

  • Claude Code (via MCP)

  • Any MCP-compatible AI agent

  • Future: LangChain, CrewAI, AutoGen

🔍 How It Differs from A2A

While inspired by Google's A2A protocol, A2AMCP makes specific design choices for AI code development:

Feature

Google A2A

A2AMCP

Protocol

HTTP-based

MCP tools

State

Stateless

Redis persistence

Focus

Generic tasks

Code development

Deployment

Per-agent servers

Single shared server

🚀 Roadmap

  • Core MCP server with Redis

  • Modern MCP SDK 1.9.3 integration

  • Fixed decorator patterns (@server.list_tools(), @server.call_tool())

  • Python SDK

  • Docker deployment

  • All 17 A2AMCP API tools implemented and tested

  • Health check endpoint for monitoring

  • Verification script for testing connectivity

  • JavaScript/TypeScript SDK

  • CLI monitoring tools

  • SplitMind native integration

  • Framework adapters (LangChain, CrewAI)

  • Enterprise features

🛠️ Troubleshooting

Agents can't see mcp__splitmind-a2amcp__ tools

  1. Restart Claude Desktop - MCP connections are established at startup

  2. Verify server is running: docker ps | grep splitmind

  3. Check health endpoint: curl http://localhost:5050/health

  4. Run verification script: python verify_mcp.py

  5. Check configuration: Ensure ~/Library/Application Support/Claude/claude_desktop_config.json contains the A2AMCP server configuration

Common Issues

  • "Tool 'X' not yet implemented" - Fixed in latest version, pull latest changes

  • Connection failed - Ensure Docker is running and ports 5050/6379 are free

  • Redis connection errors - Wait for Redis to be ready (takes ~5-10 seconds on startup)

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Development Setup

# Clone repository git clone https://github.com/webdevtodayjason/A2AMCP cd A2AMCP # Install dependencies pip install -r requirements.txt # Run tests pytest # Start development server docker-compose -f docker-compose.dev.yml up

📊 Performance

  • Handles 100+ concurrent agents

  • Sub-second message delivery

  • Automatic cleanup of dead agents

  • Horizontal scaling ready

🔒 Security

  • Project isolation

  • Optional authentication (coming soon)

  • Encrypted communication (roadmap)

  • Audit logging

📄 License

MIT License - see LICENSE file.

🙏 Acknowledgments

📞 Support

A2AMCP - Turning isolated AI agents into coordinated development teams

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A Redis-backed MCP server that enables multiple AI agents to communicate, coordinate, and collaborate while working on parallel development tasks, preventing conflicts in shared codebases.

  1. Enabling Seamless Multi-Agent Collaboration for AI-Powered Development
    1. 🚀 Quick Start
      1. Using Docker (Recommended)
      2. Configure Your Agents
    2. 🎯 What Problem Does A2AMCP Solve?
      1. Generic Use Cases Beyond SplitMind
    3. 🏗️ Architecture
      1. 🔧 Core Features
        1. 1. Real-time Agent Communication
        2. 2. File Conflict Prevention
        3. 3. Shared Context Management
        4. 4. Task Transparency
        5. 5. Multi-Project Support
        6. 6. Modern MCP Integration
      2. 📦 Installation Options
        1. Docker Compose (Production)
        2. Python SDK
        3. JavaScript/TypeScript SDK (Coming Soon)
      3. 🚦 Usage Example
        1. Python SDK
        2. Direct MCP Tool Usage
      4. 📚 Documentation
        1. 🛠️ SDKs and Tools
          1. Available Now
          2. In Development
        2. 🤝 Integration with AI Frameworks
          1. 🔍 How It Differs from A2A
            1. 🚀 Roadmap
              1. 🛠️ Troubleshooting
                1. Agents can't see mcp__splitmind-a2amcp__ tools
                2. Common Issues
              2. 🤝 Contributing
                1. Development Setup
              3. 📊 Performance
                1. 🔒 Security
                  1. 📄 License
                    1. 🙏 Acknowledgments
                      1. 📞 Support

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