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Mcp-plugins CLI/py equips your MCP with powerful prebuilt plugins

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Mcp-plugins CLI/py equips your MCP with powerful prebuilt plugins

mcp-plugins is an open source project that ships with a collection of prebuilt MCP middleware plugins to let you add logging, rate limiting, error handling, cherry pick tools you only need, human approval etc. to your MCP, within one command setup. You can use mcp-plugins cli with a config.json input to proxy on top of your MCP servers or you can use it as prebuilt middleware lib in python with FastMCP. As MCP gains more and more adoption, proxy, gateways & middleware become important pattern but there limited resources of prebuilt middleware as examples. This project is pretty early stage and experimental, but try to demonstrate a few use cases. And the provided python lib and cli will give you very easy and quick setup to try it out. You can also fork and customize the middleware there. You can check the repo README for list of prebuilt plugins and how to use it.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, open · Missing: mac, agents, macos
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
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
29%29% 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
15%15% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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