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FastMCP 2.0 – fast, Pythonic MCP servers and clients

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FastMCP 2.0 – fast, Pythonic MCP servers and clients

Hi HN -- Today I'm announcing FastMCP 2.0! A few months ago, I created the first version of FastMCP to build MCP servers in Python with minimal boilerplate. It was so successful that 1.0 was merged into the official MCP Python SDK. 2.0 builds on that foundation. While 1.0 focused on making it easy to build MCP servers, 2.0 is a major release that addresses the challenges of interacting and working with with servers as the ecosystem matures. Major new features in 2.0 include: - Server composition: either mount or import other MCP servers into your FastMCP server in order to re-expose them to clients, possibly with modifications. - Server proxying: turn any local or remote server into a FastMCP instance, letting you work with it programmatically or even run it with a different transport - Automation server generation: create a FastMCP server from an OpenAPI spec or FastAPI app - Client infrastructure: new clients for interactive use that enable advanced MCP features like client-side LLM sampling If it sounds interesting, please check out the code [1], docs [2] or announce post [3] and let me know how it goes! [1] https://github.com/jlowin/fastmcp [2] https://gofastmcp.com [3] https://www.jlowin.dev/blog/fastmcp-2

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, new, code · Missing: mac, agents, macos
70%70% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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