MCP STDIO Integration

Add STDIO Tool to AI Agent

Quick Start

Create a client file

Create a new file calculator_client.py with the following code:

from praisonaiagents import Agent, MCP

calculator_agent = Agent(
    instructions="""You are a calculator agent that can perform basic arithmetic operations.""",
    llm="gpt-4o-mini",
    tools=MCP("python calculator_server.py")
)

calculator_agent.start("What is 25 * 16?")

The command string is shell-split, so a multi-word string can be combined with args=[...] — MCP("npx -y @modelcontextprotocol/server-filesystem", args=["/tmp"]) appends the extra args after the tokens from the string. All three forms in Passing runtime arguments produce the same subprocess.

Set Up STDIO MCP Server

Create a file calculator_server.py with the following code:

# calculator_server.py
from mcp.server.fastmcp import FastMCP
import logging
import sys

# Set up logging
logging.basicConfig(level=logging.INFO, filename="calculator_server.log")
logger = logging.getLogger("calculator-server")

# Initialize FastMCP server for simple tools
mcp = FastMCP("calculator-tools")

@mcp.tool()
def add(a: float, b: float) -> float:
    """Add two numbers.

    Args:
        a: First number
        b: Second number
    
    Returns:
        The sum of a and b
    """
    logger.info(f"Adding {a} + {b}")
    return a + b

@mcp.tool()
def subtract(a: float, b: float) -> float:
    """Subtract b from a.

    Args:
        a: First number
        b: Second number
    
    Returns:
        The result of a - b
    """
    logger.info(f"Subtracting {b} from {a}")
    return a - b

@mcp.tool()
def multiply(a: float, b: float) -> float:
    """Multiply two numbers.

    Args:
        a: First number
        b: Second number
    
    Returns:
        The product of a and b
    """
    logger.info(f"Multiplying {a} * {b}")
    return a * b

@mcp.tool()
def divide(a: float, b: float) -> float:
    """Divide a by b.

    Args:
        a: First number (numerator)
        b: Second number (denominator)
    
    Returns:
        The result of a / b
    """
    if b == 0:
        raise ValueError("Cannot divide by zero")
    logger.info(f"Dividing {a} / {b}")
    return a / b

if __name__ == "__main__":
    # Run the MCP server using STDIO transport
    mcp.run()

Install Dependencies

Make sure you have the required packages installed:

pip install "praisonaiagents[llm]" mcp

Export API Key

export OPENAI_API_KEY="your_api_key"

Run the Agent

Run the agent which will automatically start the calculator server:

python calculator_client.py

Requirements

Python 3.10 or higher
MCP package

Alternative LLM Integrations

Using Groq with STDIO

from praisonaiagents import Agent, MCP

calculator_agent = Agent(
    instructions="""You are a calculator agent that can perform basic arithmetic operations.""",
    llm="groq/llama-3.2-90b-vision-preview",
    tools=MCP("python calculator_server.py")
)

calculator_agent.start("What is 144 divided by 12?")

Using Ollama with STDIO

from praisonaiagents import Agent, MCP

calculator_agent = Agent(
    instructions="""You are a calculator agent that can perform basic arithmetic operations.""",
    llm="ollama/llama3.2",
    tools=MCP("python calculator_server.py")
)

calculator_agent.start("What is 15 + 27? Use the add tool with parameters a and b.")

Gradio UI Integration

Create a Gradio UI for your calculator service:

from praisonaiagents import Agent, MCP
import gradio as gr

def calculate(query):
    calculator_agent = Agent(
        instructions="""You are a calculator agent that can perform basic arithmetic operations.""",
        llm="gpt-4o-mini",
        tools=MCP("python calculator_server.py")
    )

    result = calculator_agent.start(query)
    return f"## Calculation Result\n\n{result}"

demo = gr.Interface(
    fn=calculate,
    inputs=gr.Textbox(placeholder="What is 25 * 16?"),
    outputs=gr.Markdown(),
    title="Calculator MCP Agent",
    description="Ask any arithmetic question:"
)

if __name__ == "__main__":
    demo.launch()

Features

Simple Integration

Use standard input/output for easy integration with any tool or service.

Cross-Platform

Works on any operating system that supports Python.

Multiple LLM Options

Use with OpenAI, Groq, Ollama, or other supported LLMs.

Gradio UI

Create user-friendly interfaces for your STDIO integrations.

​Add STDIO Tool to AI Agent

​Quick Start

​Alternative LLM Integrations

​Using Groq with STDIO

​Using Ollama with STDIO

​Gradio UI Integration

​Features

Simple Integration

Cross-Platform

Multiple LLM Options

Gradio UI

Add STDIO Tool to AI Agent

Quick Start

Alternative LLM Integrations

Using Groq with STDIO

Using Ollama with STDIO

Gradio UI Integration

Features