5. Orchestrate from an LLM (MCP)

# 1. Initialize a session — note the protocol version is the MCP one.
curl -X POST http://localhost:9090/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "initialize",
    "params": {
      "protocolVersion": "2025-11-25",
      "capabilities": {},
      "clientInfo": {"name": "tutorial", "version": "1.0.0"}
    }
  }'

# 2. List the tools (substitute the session ID from the response above).
curl -X POST http://localhost:9090/mcp \
  -H "Content-Type: application/json" \
  -H "MCP-Session-Id: <session-id>" \
  -d '{
    "jsonrpc": "2.0",
    "id": 2,
    "method": "tools/list"
  }'

import asyncio

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

KUBEMQ_MCP_URL = "http://localhost:9090"


async def main() -> None:
    async with streamablehttp_client(f"{KUBEMQ_MCP_URL}/mcp") as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()

            tools = await session.list_tools()
            names = [t.name for t in tools.tools]
            print(f"{len(names)} tools available")  # 15: 11 core + 4 agent-bridge
            print("agent-bridge tools:", [n for n in names if n.startswith("agent_")])


if __name__ == "__main__":
    asyncio.run(main())

curl -X POST http://localhost:9090/mcp \
  -H "Content-Type: application/json" \
  -H "MCP-Session-Id: <session-id>" \
  -d '{
    "jsonrpc": "2.0",
    "id": 3,
    "method": "tools/call",
    "params": {
      "name": "agent_list",
      "arguments": {"skill_tags": ["research"]}
    }
  }'

result = await session.call_tool("agent_list", {"skill_tags": ["research"]})

print(f"IsError: {result.isError}")
for content in result.content:
    print(f"Result: {content.text}")

curl -X POST http://localhost:9090/mcp \
  -H "Content-Type: application/json" \
  -H "MCP-Session-Id: <session-id>" \
  -d '{
    "jsonrpc": "2.0",
    "id": 4,
    "method": "tools/call",
    "params": {
      "name": "agent_send",
      "arguments": {
        "agent_id": "research-agent-01",
        "message": "Summarize the history of message queues",
        "timeout_seconds": 60
      }
    }
  }'

result = await session.call_tool("agent_send", {
    "agent_id": "research-agent-01",
    "message": "Summarize the history of message queues",
    "timeout_seconds": 60,
})

print(f"IsError: {result.isError}")
for content in result.content:
    print(f"Result: {content.text}")