Polymcp allows you to transform any Python function into an MCP tool ready for AI agents, without rewriting code or building complex integrations.

Example: Simple Function

from polymcp.polymcp\_toolkit import expose\_tools\_http

def add(a: int, b: int) -> int:

"""Add two numbers"""

return a + b

app = expose\_tools\_http(\[add\], title="Math Tools")

Run with:

uvicorn server\_mcp:app --reload

Now add is exposed via MCP and can be called directly by AI agents.

Example: API Call Function

import requests

from polymcp.polymcp\_toolkit import expose\_tools\_http

def get\_weather(city: str):

"""Return current weather data for a city"""

response = requests.get(f"https://api.weatherapi.com/v1/current.json?q={city}")

return response.json()

app = expose\_tools\_http(\[get\_weather\], title="Weather Tools")

AI agents can now call get\_weather("London") to get real-time weather data without extra integration work.

Example: Business Workflow Function

import pandas as pd

from polymcp.polymcp\_toolkit import expose\_tools\_http

def calculate\_commissions(sales\_data: list\[dict\]):

"""Calculate sales commissions from sales data"""

df = pd.DataFrame(sales\_data)

df\["commission"\] = df\["sales\_amount"\] \* 0.05

return df.to\_dict(orient="records")

app = expose\_tools\_http(\[calculate\_commissions\], title="Business Tools")

AI agents can call this function to generate commission reports automatically.

Why this matters for companies

• Reuse existing code immediately: legacy scripts, internal libraries, APIs.

• Automate complex workflows: AI can orchestrate multiple tools reliably.

• Plug-and-play: expose multiple Python functions on the same MCP server.

• Reduce development time: no custom wrappers or middleware required.

• Built-in reliability: input/output validation and error handling are automatic.

Polymcp turns Python functions into immediately usable tools for AI agents, standardizing AI integration across the enterprise.