A2AMCP

# A2AMCP: Bringing Agent-to-Agent Communication to Model Context Protocol ## Enabling Seamless Multi-Agent Collaboration for AI-Powered Development Teams Today's AI agents are transforming how we build software, with tools like Claude Code, GitHub Copilot, and others automating complex development tasks. However, when multiple AI agents work on the same codebase simultaneously, they operate in isolation—unable to coordinate, share context, or prevent conflicts. This leads to merge conflicts, duplicated effort, and inconsistent implementations. We're introducing **A2AMCP** (Agent-to-Agent Model Context Protocol), an open-source implementation that brings Google's Agent-to-Agent (A2A) communication concepts to the Model Context Protocol ecosystem. A2AMCP enables AI agents to communicate, coordinate, and collaborate in real-time while working on parallel development tasks. ## The Challenge of Isolated AI Development In modern AI-assisted development workflows, multiple agents often work on different features simultaneously: - An authentication agent implements user login systems - A database agent designs data models - An API agent creates endpoints - A frontend agent builds user interfaces Without communication channels, these agents: - Create conflicting interfaces and type definitions - Modify the same files simultaneously, causing merge conflicts - Duplicate efforts by implementing similar functionality - Lack awareness of each other's progress and dependencies ## Introducing A2AMCP A2AMCP adapts key principles from Google's A2A protocol to the Model Context Protocol (MCP) ecosystem, creating a communication layer specifically designed for AI coding agents. Built on Redis for persistence and Docker for easy deployment, A2AMCP provides: - **Real-time agent discovery and registration** - **Inter-agent messaging and queries** - **Shared context management** (interfaces, types, API contracts) - **File locking and conflict prevention** - **Task progress visibility** through todo lists - **Multi-project isolation** with Redis namespacing ## How A2AMCP Works A2AMCP operates as a standalone MCP server that all agents connect to: ``` ┌─────────────────┐ │ A2AMCP Server │ ← Persistent Redis-backed MCP server │ (Port 5000) │ handling all agent communication └────────┬────────┘ │ ┌────┴────┬─────────┬─────────┐ ▼ ▼ ▼ ▼ ┌────────┐┌────────┐┌────────┐┌────────┐ │Agent 1 ││Agent 2 ││Agent 3 ││Agent N │ │Auth ││Profile ││API ││Frontend│ └────────┘└────────┘└────────┘└────────┘ ``` Each agent: 1. Registers itself with project and task information 2. Creates a todo list for transparency 3. Communicates with other agents via MCP tools 4. Shares interfaces and coordinates file access 5. Maintains heartbeat for liveness detection ## Key Capabilities from A2A A2AMCP implements several core concepts from Google's A2A protocol, adapted for the MCP ecosystem: ### 1. **Agent Discovery and Registration** Like A2A's agent cards, each A2AMCP agent registers with: - Unique session identifier - Task description and capabilities - Project namespace for isolation - Current status and progress ### 2. **Message-Based Communication** Inspired by A2A's client-server messaging: - Direct queries between specific agents - Broadcast messages to all project agents - Asynchronous response handling with timeouts - Message queuing for offline agents ### 3. **Task Lifecycle Management** Similar to A2A's task states: - Agents break work into observable todos - Status progression (pending → in_progress → completed) - Progress visibility across all agents - Automatic cleanup on completion ### 4. **Capability Sharing** Adapting A2A's capability discovery: - Shared interface definitions - API contract registration - File dependency tracking - Real-time updates on changes ## Key Differences from Google's A2A While inspired by A2A, A2AMCP makes several architectural decisions specific to AI coding agents: ### 1. **MCP-Native Implementation** - **A2A**: HTTP-based protocol for cross-framework compatibility - **A2AMCP**: MCP tools for native Claude/AI agent integration ### 2. **Persistent State Management** - **A2A**: Stateless HTTP interactions - **A2AMCP**: Redis-backed persistence surviving agent restarts ### 3. **File-System Awareness** - **A2A**: Generic task coordination - **A2AMCP**: Specific file locking and conflict prevention for code ### 4. **Todo-Based Transparency** - **A2A**: Abstract task management - **A2AMCP**: Visible todo lists showing exact progress ### 5. **Project Namespacing** - **A2A**: Enterprise application focus - **A2AMCP**: Multi-project development isolation ## Real-World Example Consider building an e-commerce platform with multiple AI agents: **Agent 1 (Authentication)** registers and starts work: ```python register_agent("ecommerce-v2", "task-auth-001", "001", "feature/auth", "Build authentication") add_todo("ecommerce-v2", "task-auth-001", "Create User model", 1) add_todo("ecommerce-v2", "task-auth-001", "Implement JWT tokens", 1) ``` **Agent 2 (User Profiles)** starts and discovers Agent 1: ```python list_active_agents("ecommerce-v2") # Sees auth agent is active get_all_todos("ecommerce-v2") # Sees auth agent is creating User model # Waits for User model, then queries query_agent("ecommerce-v2", "task-profile-002", "task-auth-001", "interface", "What fields does User have?") ``` **Agent 1** shares its interface: ```python register_interface("ecommerce-v2", "task-auth-001", "User", "interface User { id: string; email: string; role: string; }") ``` Now all agents can immediately access this shared definition, preventing inconsistencies and enabling seamless integration. ## Implementation Architecture A2AMCP consists of three main components: ### 1. **MCP Server** (Python) - Handles all agent communication - Implements MCP tool definitions - Manages heartbeat monitoring - Provides automatic cleanup ### 2. **Redis Backend** - Persistent state storage - Project namespace isolation - Message queuing - Real-time pub/sub capabilities ### 3. **Docker Deployment** - Single-command deployment - Pre-configured networking - Optional monitoring UI - Production-ready configuration ## Getting Started Deploy A2AMCP in minutes: ```bash # 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 a2amcp-server ``` Configure your AI agents to use A2AMCP by adding MCP tools to their context and including communication instructions in their prompts. ## Benefits for Development Teams ### 1. **Eliminate Merge Conflicts** File locking ensures only one agent modifies a file at a time. ### 2. **Consistent Interfaces** Shared type definitions prevent incompatible implementations. ### 3. **Transparent Progress** Todo lists show exactly what each agent is working on. ### 4. **Faster Development** Agents can query each other instead of making assumptions. ### 5. **Project Isolation** Multiple projects can use the same A2AMCP server without interference. ## Open Source and Extensible A2AMCP is released as open source under the MIT license. We welcome contributions from the community to: - Add new communication patterns - Implement additional MCP tools - Improve scaling and performance - Create integrations with other AI tools - Develop monitoring dashboards ## Future Roadmap We're excited about expanding A2AMCP's capabilities: - **GraphQL API** for external monitoring - **WebSocket support** for real-time updates - **Agent templates** for common patterns - **Conflict resolution** strategies - **Cross-project dependencies** - **Performance analytics** ## Join the A2AMCP Community A2AMCP represents a new paradigm in multi-agent AI development, where agents work together as a coordinated team rather than isolated workers. By bringing A2A concepts to the MCP ecosystem, we're enabling a future where AI agents can collaborate as effectively as human developers. - **GitHub**: [github.com/webdevtodayjason/A2AMCP](https://github.com/webdevtodayjason/A2AMCP) - **Documentation**: Full API reference and examples - **Discord**: Join our community discussions - **Contributing**: See CONTRIBUTING.md for guidelines ## Acknowledgments A2AMCP is inspired by Google's Agent-to-Agent Protocol (A2A) and built specifically for the Model Context Protocol ecosystem. We thank the teams at Google and Anthropic for their pioneering work in agent interoperability and context protocols. --- *A2AMCP is an open-source project enabling AI agents to work together effectively. Join us in building the future of collaborative AI development.*