I built PolyMCP, a framework that lets you transform any Python or TypeScript function into an MCP (Model Context Protocol) tool ready to be used by AI agents — no rewriting, no complex integrations.

Examples

Python 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

AI agents can now call add(3, 4) directly.

TypeScript function:

import { z } from 'zod'; import { tool, exposeToolsHttp } from 'polymcp';

const uppercaseTool = tool({ name: 'uppercase', description: 'Convert text to uppercase', inputSchema: z.object({ text: z.string() }), function: async ({ text }) => text.toUpperCase(), });

const app = exposeToolsHttp([uppercaseTool], { title: "Text Tools" }); app.listen(3000);

Agents can call uppercase("hello") and get "HELLO" instantly.

Business workflow function (Python example):

import pandas as pd from polymcp.polymcp_toolkit import expose_tools_http

def calculate_commissions(sales_data: list[dict]): 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")

Now AI agents can generate reports automatically.

Why it matters • Reuse existing code immediately: legacy scripts, internal libraries, APIs. • Automate workflows: AI can orchestrate multiple tools reliably. • Cross-language: expose Python and TypeScript functions on the same MCP server. • Plug-and-play: no custom wrappers or middleware needed. • Built-in reliability: input/output validation and error handling included.

PolyMCP makes any Python or TypeScript function immediately usable by AI agents, standardizing integration across enterprise software.

Repo: https://github.com/poly-mcp/Polymcp

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