by webdevtodayjason
A2AMCP synchronizes multiple AI agents on shared codebases with Redis-powered messaging, file locking, interface sharing
Coordinates multiple AI agents working on the same codebase through Redis-backed infrastructure, preventing conflicts with file locking and enabling real-time communication between agents.
A2AMCP is a community-built MCP server published by webdevtodayjason that provides AI assistants with tools and capabilities via the Model Context Protocol. A2AMCP synchronizes multiple AI agents on shared codebases with Redis-powered messaging, file locking, interface sharing It is categorized under ai ml, developer tools.
You can install A2AMCP in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
A2AMCP is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We evaluated A2AMCP against two servers with overlapping tools; this profile had the clearer scope statement.
A2AMCP has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
A2AMCP is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
A2AMCP reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired A2AMCP into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Useful MCP listing: A2AMCP is the kind of server we cite when onboarding engineers to host + tool permissions.
Useful MCP listing: A2AMCP is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated A2AMCP against two servers with overlapping tools; this profile had the clearer scope statement.
Useful MCP listing: A2AMCP is the kind of server we cite when onboarding engineers to host + tool permissions.
A2AMCP reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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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.
# 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
# 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
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"
}
}
}
}
When multiple AI agents work on the same codebase:
A2AMCP can coordinate any multi-agent scenario:
┌─────────────────┐
│ 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│
└────────┘└────────┘└────────┘└────────┘
@server.list_tools() and @server.call_tool() patternsservices:
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
pip install a2amcp-sdk
npm install @a2amcp/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; }"
)
# 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 {...}")
See SDK Development Progress for details.
A2AMCP is designed to work with:
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 |
@server.list_tools(), @server.call_tool())mcp__splitmind-a2amcp__ toolsdocker ps | grep splitmindcurl http://localhost:5050/healthpython verify_mcp.py~/Library/Application Support/Claude/claude_desktop_config.json contains the A2AMCP server configurationWe welcome contributions! See CONTRIBUTING.md for guidelines.
# 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
MIT License - see LICENSE file.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.