FastMCP – A Pythonic way to build Model Context Protocol servers
Hi HN -- Since Anthropic announced the Model Context Protocol (MCP) last week [1], I've been excited about giving Claude new capabilities through my custom servers. But while MCP is powerful, implementing the protocol correctly requires a lot of low-level boilerplate code. I found myself wanting something like FastAPI - a high-level framework that would let me focus on building features, not servers. After some hacking, I'm sharing FastMCP: a Pythonic framework for building MCP servers. FastMCP uses decorators to transform normal functions into MCP tools, resources, templates, and prompts,…
FastMCP – A Pythonic way to build Model Context Protocol servers launched on December 1, 2024 with 6 votes and more than 50% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
🚀 The fast, Pythonic way to build MCP servers and clients. - PrefectHQ/fastmcp
In the maker’s words, at launch
Hi HN -- Since Anthropic announced the Model Context Protocol (MCP) last week [1], I've been excited about giving Claude new capabilities through my custom servers. But while MCP is powerful, implementing the protocol correctly requires a lot of low-level boilerplate code. I found myself wanting something like…
Does a similar job
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AI-powered web service combining FastAPI, Pydantic-AI, and MCP servers2025 · github.com · ▲46Hey all! I recently gave a workshop talk at PyCon Greece 2025 about building production-ready agent systems. To check the workshop, I put together a demo repo: (I will add the slides too soon in my blog: https://www.petrostechchronicles.com/) https://github.com/Aherontas/Pycon_Greece_2025_Presentation_... The idea was to show how multiple AI agents can collaborate using FastAPI + Pydantic-AI, with protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) for safe communication and orchestration. Features: - Multiple agents running in containers -…
Mcp-Agent – Build effective agents with Model Context Protocol2025 · github.com · ▲80Hey HN, I spent my xmas break building an agent framework called mcp-agent [1](https://github.com/lastmile-ai/mcp-agent) for Model Context Protocol [2]. It makes it easy to build AI apps with MCP servers, and implements every pattern from the popular Building Effective Agents blog [3] as well as OpenAI’s Swarm [4]. I’m sharing it early to get community feedback on where to take it from here, and to ask for contributions. For those who aren’t familiar with MCP, I think of it as a standardized interface to let AI communicate with software via tool calls, resources and…
An MCP server that gives LLMs temporal awareness and time calculation2025 · github.com · ▲91This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time. Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models perception — extra senses beyond the prompt text. Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo. If time works, what else could we surface? - Location /…
- PAPrefab – A generative UI framework for PythonApr 2026 · prefab.prefect.io · ▲5
Hi HN, I'm the author of FastMCP, the most popular Python framework for building MCP servers. I've been really excited about MCP Apps for a while. I think letting a server ship a fully interactive UI directly into the conversation is one of the most compelling additions to the protocol. I wanted to make this a first-class experience in FastMCP, but I kept getting stuck on what it actually means for a Python framework to integrate with a frontend feature. The JavaScript ecosystem has extraordinary tooling for this. I didn't want to build a worse version of it just to stay in Python. What…
- PTPolymcp – Turn Any Python Function into an MCP Tool for AI AgentsJan 2026 · ▲23
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…
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Source: Show HN post ↗ · launch text quoted in part
