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poly-mcp
polymcp
HISTORICAL RECORD2025–2026
poly-mcp / polymcp · Not publicly available

polymcp

Discover this repository through its Hacker News conversations.

2Distinct people
26Mentions
0Explicit recommenders
REPOSITORY AT A GLANCE

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This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 9:28:18 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week26 dated mentions

Observed mentions: 2025-11-29 15:28:11 UTC to 2026-02-19 22:07:49 UTC. Weeks start Monday, UTC.

02462025-11-24 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsNov 242025-12-01 00:00:00 UTC to 2025-12-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-08 00:00:00 UTC to 2025-12-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-15 00:00:00 UTC to 2025-12-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-22 00:00:00 UTC to 2025-12-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-29 00:00:00 UTC to 2026-01-05 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-01-05 00:00:00 UTC to 2026-01-12 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJan 52026-01-12 00:00:00 UTC to 2026-01-19 00:00:00 UTC (exclusive): 4 mentions; 4 submissions, 0 comments2026-01-19 00:00:00 UTC to 2026-01-26 00:00:00 UTC (exclusive): 5 mentions; 5 submissions, 0 comments2026-01-26 00:00:00 UTC to 2026-02-02 00:00:00 UTC (exclusive): 4 mentions; 4 submissions, 0 comments2026-02-02 00:00:00 UTC to 2026-02-09 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-02-09 00:00:00 UTC to 2026-02-16 00:00:00 UTC (exclusive): 7 mentions; 6 submissions, 1 comments2026-02-16 00:00:00 UTC to 2026-02-23 00:00:00 UTC (exclusive): 3 mentions; 3 submissions, 0 commentsFeb 16

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Nov 242025-11-24 00:00:00 UTC2025-12-01 00:00:00 UTC110
Dec 12025-12-01 00:00:00 UTC2025-12-08 00:00:00 UTC000
Dec 82025-12-08 00:00:00 UTC2025-12-15 00:00:00 UTC000
Dec 152025-12-15 00:00:00 UTC2025-12-22 00:00:00 UTC000
Dec 222025-12-22 00:00:00 UTC2025-12-29 00:00:00 UTC000
Dec 292025-12-29 00:00:00 UTC2026-01-05 00:00:00 UTC101
Jan 52026-01-05 00:00:00 UTC2026-01-12 00:00:00 UTC000
Jan 122026-01-12 00:00:00 UTC2026-01-19 00:00:00 UTC440
Jan 192026-01-19 00:00:00 UTC2026-01-26 00:00:00 UTC550
Jan 262026-01-26 00:00:00 UTC2026-02-02 00:00:00 UTC440
Feb 22026-02-02 00:00:00 UTC2026-02-09 00:00:00 UTC110
Feb 92026-02-09 00:00:00 UTC2026-02-16 00:00:00 UTC761
Feb 162026-02-16 00:00:00 UTC2026-02-23 00:00:00 UTC330

What Hacker News says

5 sources
justvugg·Jan 25, 2026Submission

Show HN:PolyMCP – from Server to WebAssembly from the Same Python Code

PolyMCP now lets you expose Python MCP (Model Context Protocol) tools in two ways from the same code: 1. Server-based MCP deployments (HTTP endpoints) 2. WebAssembly bundles that run directly in the browser This means you can reuse the same Python functions for server workflows or client-side experiments without any code changes. ⸻ Why it matters • Plug-and-play: one Python function, multiple deployment options. • Instant testing: run your tools locally in the browser or via HTTP. • Enterprise-ready: leverage internal libraries, scripts, and APIs immediately. • Unified interface: MCP agents call your tools the same way, regardless of server or WASM. ⸻ Example # Python function def calculate_stats(numbers): """Return basic statistics for a list of numbers""" return { "count": len(numbers), "sum": sum(numbers),
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"mean": sum(numbers)/len(numbers) } # === WebAssembly MCP === from polymcp import expose_tools_wasm compiler = expose_tools_wasm([calculate_stats]) compiler.compile("./wasm_output") The same function can also be exposed via HTTP MCP endpoints without modification. # === HTTP MCP === from polymcp.polymcp_toolkit import expose_tools app = expose_tools([calculate_stats], title="Stats Tools") # Run with: uvicorn server_mcp:app --reload ⸻ Repo: https://github.com/poly-mcp/Polymcp We’d love to hear how you’re using PolyMCP and feature ideas for future releases.
1 points · Open discussion
justvugg·Jan 24, 2026Submission

Show HN: Polymcp – Turn Any Python Function into an MCP Tool for AI Agents

I built Polymcp, a framework that allows you to transform any Python function into an MCP (Model Context Protocol) tool ready to be used by AI agents. No rewriting, no complex integrations. Examples 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. API 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
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can call get_weather("London") to get real-time weather data instantly. 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 now generate commission reports automatically. Why it matters for companies • Reuse existing code immediately: legacy scripts, internal libraries, APIs. • Automate complex workflows: AI can orchestrate multiple tools reliably. • Plug-and-play: multiple Python functions exposed on the same MCP server. • Reduce development time: no custom wrappers or middleware needed. • Built-in reliability: input/output validation and error handling included. Polymcp makes Python functions immediately usable by AI agents, standardizing integration across enterprise software. Repo: https://github.com/poly-mcp/Polymcp
23 points · Open discussion
justvugg·Jan 23, 2026Submission

Show HN: PolyMCP – Simplifying MCP Server Development and Agent Integration

Hi HN, I built PolyMCP, a framework to make building MCP (Model Context Protocol) servers and integrating agents much easier. Working with MCP and agent tooling often comes with repetitive or frustrating challenges: •Exposing Python functions as discoverable tools can be tedious. •Orchestrating multiple MCP servers usually requires a lot of glue code. •Debugging multi-server setups is tricky without good visibility into calls, inputs, and outputs. •Integrating agents with LLMs to automatically discover and call tools is still immature in most setups. PolyMCP addresses these pain points: •Flexible tool exposure – Expose Python functions as MCP tools with minimal boilerplate. Supports HTTP, in-process, or stdio execution. •Real-time Inspector – Monitor tool calls live, inspect inputs/outputs, and test interactions interactively. •Built-in a
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gent support – Agents can automatically discover and invoke tools with multiple LLM providers. •CLI and workflow tooling – Quickly scaffold, test, and run MCP projects without tedious setup. The goal is to make MCP server development and multi-tool agent orchestration faster and less painful. Check it out here: https://github.com/poly-mcp/Polymcp I’d love to hear feedback, ideas, or suggestions!
2 points · Open discussion
justvugg·Jan 21, 2026Submission

Show HN: PolyMCP – open-source toolkit to expose MCP tools and run agents

Hi HN — I’m the author of PolyMCP (MIT). PolyMCP is a toolkit/runtime for the Model Context Protocol (MCP): it helps you build MCP tool servers (expose Python functions as tools over HTTP/stdio), connect multiple MCP servers, and run agents that orchestrate them with deploy-friendly guardrails. Latest update is mostly reliability work: - OAuth2 (RFC 6749): client credentials + authorization code flows, refresh handling, basic retries - Docker executor cleanup fixes (Windows + Unix) - Better skills/tool matching + stdio server support - CodeAgent async handling + error recovery - Health checks, structured logging, and rate limiting Repo: https://github.com/poly-mcp/Polymcp If you’ve deployed MCP/agent systems: what tends to bite you first (auth, rate limits, observability, sandboxing)? I’m especially interested in OAuth2 edge cases and con
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tainer lifecycle issues.
1 points · Open discussion

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