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Deploy single or multi-agent systems as MCP (Model Context Protocol) servers for Claude Desktop, Cursor, and other MCP clients.

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Quick Start

1

Install Dependencies

pip install "praisonaiagents[mcp]"
2

Set API Key

export OPENAI_API_KEY="your-key"
3

Create Agent MCP Server

from praisonaiagents import Agent

agent = Agent(
    name="TweetAgent",
    instructions="Create engaging tweets",
    llm="gpt-4o-mini"
)
agent.launch(port=8080, protocol="mcp")
4

Verify

curl http://localhost:8080/sse

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Single Agent as MCP

from praisonaiagents import Agent

agent = Agent(
    name="TweetAgent",
    instructions="Create engaging tweets",
    llm="gpt-4o-mini"
)
agent.launch(port=8080, protocol="mcp")
Expected Output: 
🚀 Agent 'TweetAgent' MCP server starting on http://0.0.0.0:8080
📡 MCP SSE endpoint available at /sse
📢 MCP messages post to /messages/
🛠️ Available MCP tools: execute_tweetagent_task

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Multi-Agent as MCP

from praisonaiagents import Agent, Agents

researcher = Agent(name="Researcher", instructions="Research topics", llm="gpt-4o-mini")
writer = Agent(name="Writer", instructions="Write content", llm="gpt-4o-mini")

agents = Agents(agents=[researcher, writer])
agents.launch(port=8080, protocol="mcp")
Expected Output: 
🚀 PraisonAIAgents MCP Workflow server starting on http://0.0.0.0:8080
📡 MCP SSE endpoint available at /sse
📢 MCP messages post to /messages/
🛠️ Available MCP tools: execute_workflow
🔄 Agents in MCP workflow: Researcher, Writer

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MCP Endpoints

EndpointMethodDescription
/sseGETSSE connection for MCP
/messages/POSTSend MCP messages

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launch() Parameters

ParameterTypeDefaultDescription
portint8000Server port
hoststr0.0.0.0Server host
protocolstrmcpMust be mcp
debugboolFalseDebug mode

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Connect MCP Client

Claude Desktop (claude_desktop_config.json): 
{
  "mcpServers": {
    "praisonai-agent": {
      "url": "http://localhost:8080/sse"
    }
  }
}
Cursor (.cursor/mcp.json): 
{
  "mcpServers": {
    "praisonai-agent": {
      "url": "http://localhost:8080/sse"
    }
  }
}

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Test MCP Tools

# List tools
curl -X POST http://localhost:8080/messages/ \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "method": "tools/list", "id": 1}'

# Call tool
curl -X POST http://localhost:8080/messages/ \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {"name": "execute_tweetagent_task", "arguments": {"task": "Create a tweet about AI"}},
    "id": 2
  }'

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Docker Deployment

app.py: 
from praisonaiagents import Agent

agent = Agent(instructions="Create tweets", llm="gpt-4o-mini")
agent.launch(port=8080, protocol="mcp")
Dockerfile: 
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY app.py .
EXPOSE 8080
CMD ["python", "app.py"]
requirements.txt: 
praisonaiagents[mcp]
Build and run: 
docker build -t agent-mcp-server .
docker run -p 8080:8080 -e OPENAI_API_KEY=$OPENAI_API_KEY agent-mcp-server

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Troubleshooting

IssueFix
Port in uselsof -i :8080
Missing depspip install "praisonaiagents[mcp]"
No API keyexport OPENAI_API_KEY="your-key"
SSE not connectingCheck firewall, use host="0.0.0.0"