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MCP Tools Integration

The MCP (Model Context Protocol) module enables seamless integration of MCP-compliant tools and servers with PraisonAI agents, supporting both stdio and SSE transport methods.

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Overview

MCP (Model Context Protocol) is a standard for connecting AI assistants to external tools and data sources. The MCP module in PraisonAI provides:
  • Stdio Transport: Run MCP servers as subprocess commands
  • SSE Transport: Connect to HTTP/SSE-based MCP servers
  • Automatic Tool Discovery: Tools are automatically discovered and made available to agents
  • Flexible Integration: Support for NPX packages, Python scripts, and remote servers

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Quick Start

from praisonaiagents import Agent, MCP

# Use the memory MCP server from NPX
agent = Agent(
    name="Memory Assistant",
    instructions="You can store and retrieve memories.",
    tools=MCP("npx @modelcontextprotocol/server-memory")
)

response = agent.start("Remember that my favourite colour is blue")

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Transport Methods

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Stdio Transport

The stdio transport runs MCP servers as subprocesses: 
# Simple command string
tools = MCP("npx @modelcontextprotocol/server-github")

# Python script
tools = MCP("python3 mcp_weather_server.py")

# With environment variables
import os
os.environ["API_KEY"] = "your-api-key"
tools = MCP("node weather-server.js")

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SSE Transport

The SSE transport connects to HTTP endpoints using Server-Sent Events: 
# Simple SSE endpoint
tools = MCP("http://localhost:8080/sse")

# HTTPS endpoint
tools = MCP("https://api.example.com/mcp/sse")

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Available MCP Servers

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Official NPX Servers

npx @modelcontextprotocol/server-memory
Store and retrieve conversation memories

npx @modelcontextprotocol/server-filesystem
Read and write files with safety controls

npx @modelcontextprotocol/server-github
Interact with GitHub repositories

npx @modelcontextprotocol/server-postgres
Query and manage PostgreSQL databases

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Custom Python Servers

Create your own MCP server in Python: 
# mcp_calculator.py
import asyncio
from mcp import Server, Tool

server = Server("calculator")

@server.tool
async def add(a: float, b: float) -> float:
    """Add two numbers"""
    return a + b

@server.tool
async def multiply(a: float, b: float) -> float:
    """Multiply two numbers"""
    return a * b

if __name__ == "__main__":
    asyncio.run(server.run())
Use in agent: 
agent = Agent(
    tools=MCP("python3 mcp_calculator.py")
)

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Tool Discovery

MCP automatically discovers available tools: 
# Create MCP instance
mcp = MCP("npx @modelcontextprotocol/server-filesystem")

# Tools are automatically discovered and can be iterated
for tool in mcp:
    print(f"Tool: {tool.name}")
    print(f"Description: {tool.description}")
    print(f"Parameters: {tool.parameters}")

# Assign to agent
agent = Agent(name="File Manager", tools=mcp)

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Error Handling

MCP includes built-in error handling and retry logic for robust operation. 
try:
    agent = Agent(
        tools=MCP("npx @modelcontextprotocol/server-memory")
    )
    response = agent.start("Store this information")
except Exception as e:
    print(f"MCP Error: {e}")
    # Fallback logic

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Advanced Usage

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Multiple MCP Servers

from praisonaiagents import Agent, MCP

# Combine multiple MCP servers
memory_tools = MCP("npx @modelcontextprotocol/server-memory")
file_tools = MCP("npx @modelcontextprotocol/server-filesystem")
custom_tools = MCP("python3 custom_mcp.py")

agent = Agent(
    name="Multi-Tool Assistant",
    instructions="You can manage files and memories.",
    tools=[*memory_tools, *file_tools, *custom_tools]
)

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Environment Configuration

import os

# Set environment variables for MCP servers
os.environ["GITHUB_TOKEN"] = "your-github-token"
os.environ["DATABASE_URL"] = "postgresql://..."

# MCP servers can access these variables
github_tools = MCP("npx @modelcontextprotocol/server-github")
db_tools = MCP("python3 database_mcp.py")

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Debugging MCP Connections

# Enable debug mode
mcp = MCP(
    "npx @modelcontextprotocol/server-memory",
    debug=True  # Prints detailed logs
)

# Check if tools are loaded
if not list(mcp):
    print("No tools discovered from MCP server")

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Creating SSE MCP Servers

Example SSE server implementation: 
# sse_mcp_server.py
from flask import Flask, Response
import json

app = Flask(__name__)

@app.route('/sse')
def sse():
    def generate():
        # Send tool definitions
        tools = [{
            "name": "get_weather",
            "description": "Get weather information",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }]
        
        yield f"data: {json.dumps({'type': 'tools', 'tools': tools})}\n\n"
    
    return Response(generate(), mimetype="text/event-stream")

if __name__ == '__main__':
    app.run(port=8080)

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Best Practices

Transport Choice

  • Use stdio for local tools and development
  • Use SSE for remote/cloud deployments
  • Consider latency and reliability needs

Reliability

  • Implement timeouts for long-running operations
  • Handle server disconnections gracefully
  • Provide fallback options

Security

  • Validate input/output from MCP servers
  • Use environment variables for secrets
  • Implement proper authentication for SSE

Performance

  • Reuse MCP instances when possible
  • Monitor subprocess resource usage
  • Implement connection pooling for SSE

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Example: Multi-Tool Agent

from praisonaiagents import Agent, Task, PraisonAIAgents, MCP
import os

# Set up environment
os.environ["GITHUB_TOKEN"] = "ghp_..."

# Create agent with multiple MCP tools
agent = Agent(
    name="DevOps Assistant",
    instructions="""You are a DevOps assistant that can:
    - Manage files and directories
    - Interact with GitHub repositories
    - Store and retrieve important information
    Use your tools wisely to help with development tasks.""",
    tools=[
        MCP("npx @modelcontextprotocol/server-filesystem"),
        MCP("npx @modelcontextprotocol/server-github"),
        MCP("npx @modelcontextprotocol/server-memory")
    ]
)

# Create tasks
tasks = [
    Task(
        description="Check the current directory structure",
        agent=agent,
        expected_output="Directory listing with key files identified"
    ),
    Task(
        description="Remember the project structure for future reference",
        agent=agent,
        expected_output="Confirmation that structure is memorised"
    )
]

# Run the system
agents = PraisonAIAgents(agents=[agent], tasks=tasks)
result = agents.start()

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Next Steps

Create custom tools for agents

Explore available MCP servers