I built PolyMCP, an open-source framework around the Model Context Protocol (MCP) that lets you turn any existing Python function into an MCP tool usable by AI agents — with no rewrites, no glue code, no custom wrappers.

Over the last weeks, PolyMCP has grown into a small ecosystem: • PolyMCP (core) – expose Python functions as MCP tools • PolyMCP Inspector – visual UI to explore, test, and debug MCP servers • PolyMCP MCP SDK Apps – build MCP-powered apps with tools + UI resources

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1) Turn any Python function into an MCP tool

Basic example:

from polymcp import expose_tools_http

def add(a: int, b: int) -> int: """Add two numbers""" return a + b

app = expose_tools_http( tools=[add], title="Math Tools" )

Run it:

uvicorn server_mcp:app --reload

Now add is an MCP-compliant tool that any AI agent can discover and call.

No decorators, no schema files, no agent-specific SDKs.

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2) Real APIs, not toy examples

Existing API code works as-is:

import requests from polymcp 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")

Agents can now call:

get_weather("London")

and receive real-time data.

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3) Business & internal workflows

Example: internal reporting logic reused directly by agents.

import pandas as pd from polymcp 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")

No rewriting legacy logic.

4) PolyMCP Inspector (visual debugging)

To make MCP development usable in practice, I added PolyMCP Inspector: • Visual UI to browse tools, prompts, and resources • Call MCP tools interactively • Inspect schemas, inputs, outputs, and errors • Multi-server support (HTTP + stdio) • Built-in chat playground (OpenAI / Anthropic / Ollama)

Think “Postman + DevTools” for MCP servers.

Repo: https://github.com/poly-mcp/PolyMCP-Inspector

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5) MCP SDK Apps (tools + UI)

The latest addition is PolyMCP MCP SDK Apps: • Build MCP apps, not just tools • Expose: • tools • UI resources (HTML/JS dashboards) • app-level workflows • Let agents interact with both tools and UIs

This is useful for: • internal copilots • ops dashboards • support tools • enterprise AI frontends

Repo: https://github.com/poly-mcp/PolyMCP-MCP-SDK-Apps

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Why this matters (especially for companies) • Reuse existing code immediately (scripts, APIs, internal libs) • Standard MCP interface instead of vendor-specific agent SDKs • Multiple tools, one server • Agent-driven orchestration, not hardcoded flows • Faster AI adoption without refactoring everything

PolyMCP treats AI agents as clients of your software, not magic wrappers around it.

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Repos • Core framework: https://github.com/poly-mcp/PolyMCP • Inspector UI: https://github.com/poly-mcp/PolyMCP-Inspector • MCP SDK Apps: https://github.com/poly-mcp/PolyMCP-MCP-SDK-Apps

Happy to hear feedback from people building MCP servers, agents, or internal AI tools.