Show HN: PolyMCP – AI-Callable Python and TS Tools with Inspector and Apps

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