agent-mesh-mcp
Enables inter-agent communication via RabbitMQ, allowing agents to ask and answer questions routed by topic.
README
agent-mesh-mcp
MCP server for inter-agent communication via RabbitMQ. Agents ask each other questions routed by topic.
Install
npm install -g agent-mesh-mcp
Usage
Claude Code (~/.claude.json)
{
"mcpServers": {
"agent-mesh": {
"command": "agent-mesh-mcp",
"env": {
"RABBITMQ_URL": "amqp://user:pass@your-rabbitmq:5672/",
"AGENT_NAME": "human"
}
}
}
}
Cursor (.cursor/mcp.json)
{
"mcpServers": {
"agent-mesh": {
"command": "agent-mesh-mcp",
"env": {
"RABBITMQ_URL": "amqp://user:pass@your-rabbitmq:5672/",
"AGENT_NAME": "cursor"
}
}
}
}
npx (no install)
{
"mcpServers": {
"agent-mesh": {
"command": "npx",
"args": ["-y", "agent-mesh-mcp"],
"env": {
"RABBITMQ_URL": "amqp://user:pass@your-rabbitmq:5672/"
}
}
}
}
Tool
ask_agent(topic, question)
Ask another agent a question. The question is published to a RabbitMQ topic exchange and routed to the agent listening on that topic.
Parameters:
topic— routing key (e.g.backend,frontend,ops,qa,devops)question— the question to ask
Returns: the agent's reply, or a timeout message.
How It Works
Caller ──► ask_agent(topic, question)
│
▼
RabbitMQ (topic exchange)
│
▼
Worker on ask.<topic> queue
│
▼
Agent processes question
│
▼
Reply ──► RabbitMQ ──► Caller
Each agent is a worker process listening on its topic queue. When a question arrives, the worker runs an AI model (e.g. Claude) to answer it, then publishes the reply back.
Environment Variables
| Variable | Default | Description |
|---|---|---|
RABBITMQ_URL |
amqp://guest:guest@localhost:5672/ |
RabbitMQ connection URL |
AGENT_NAME |
unknown |
Name of the calling agent |
ASK_TIMEOUT |
900 |
Reply timeout in seconds |
EXCHANGE_NAME |
agents |
RabbitMQ exchange name |
License
MIT
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