Server: Agents MCP

Publish single or multi-agent systems over MCP with launch(protocol="mcp") — the built-in idiom that delegates to serve_agents(...) under the hood.

Same code path as serve_agents([...]). launch(protocol="mcp") calls serve_agents(...) for you, so the published tools (ask_{name} + list_agents), the /mcp endpoint, and per-session continuity are identical. Pick whichever idiom fits your code — a method on the Agent/Agents object, or the standalone function.

Quick Start

Install Dependencies

pip install praisonai-mcp

praisonai-mcp is an optional package imported lazily. Without it, launch(protocol="mcp") prints: “MCP serving requires the ‘praisonai-mcp’ package.”

Set API Key

export OPENAI_API_KEY="your-key"

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")

Verify

curl http://localhost:8080/mcp

Which idiom fits your code?

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")
# Equivalent to: serve_agents([agent], port=8080)

Published tools: ask_tweetagent + list_agents. The output comes from praisonai-mcp and the endpoint is /mcp.

Multi-Agent as MCP

from praisonaiagents import Agent
from praisonaiagents.agents import PraisonAIAgents

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

agents = PraisonAIAgents(agents=[researcher, writer])
agents.launch(port=8080, protocol="mcp")
# Equivalent to: serve_agents([researcher, writer], port=8080)

Each agent gets its own ask_{name} tool — here ask_researcher and ask_writer — plus one shared list_agents.

path is ignored in MCP mode. protocol="http" behaviour is unchanged.

MCP Endpoints

Endpoint	Method	Description
`/mcp`	POST	JSON-RPC over spec-compliant streamable HTTP

See Serve Agents for the canonical tool schema and session model.

launch() Parameters

Parameter	Type	Default	Description
`port`	int	`8000`	Server port
`host`	str	`0.0.0.0`	Server host
`protocol`	str	`mcp`	Must be `mcp`
`debug`	bool	`False`	Debug mode

Connect MCP Client

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "praisonai-agent": {
      "url": "http://localhost:8080/mcp",
      "transport": "http-stream"
    }
  }
}

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "praisonai-agent": {
      "url": "http://localhost:8080/mcp",
      "transport": "http-stream"
    }
  }
}

Test MCP Tools

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

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

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
praisonai-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

Troubleshooting

Issue	Fix
Port in use	`lsof -i :8080`
`MCP serving requires the 'praisonai-mcp' package.`	`pip install praisonai-mcp`
No API key	`export OPENAI_API_KEY="your-key"`
Client can’t connect	Check firewall, use `host="0.0.0.0"`

Serve Agents - The serve_agents([...]) function launch() delegates to
API Vocabulary - One page for approval= and launch(protocol=...)
Tools MCP - Deploy tools as MCP server
Recipes MCP - Deploy recipes as MCP server
PraisonAI MCP - Full PraisonAI MCP server
Agents HTTP - Deploy as HTTP server

​Quick Start

​Which idiom fits your code?

​Single Agent as MCP

​Multi-Agent as MCP

​MCP Endpoints

​launch() Parameters

​Connect MCP Client

​Test MCP Tools

​Docker Deployment

​Troubleshooting

​Related

Quick Start

Which idiom fits your code?

Single Agent as MCP

Multi-Agent as MCP

MCP Endpoints

launch() Parameters

Connect MCP Client

Test MCP Tools

Docker Deployment

Troubleshooting

Related