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PolyMCP – Expose Python and TypeScript Functions as MCP Tools

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PolyMCP – Expose Python and TypeScript Functions as MCP Tools

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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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, 000 · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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