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Self-Hosted OAuth Authentication Library for MCP Servers

Hacker News

Self-Hosted OAuth Authentication Library for MCP Servers

Hey HN, The latest MCP spec ( https://modelcontextprotocol.io/specification/2025-06-18/bas... ) requires MCP Servers implement OAuth for authenticating users and authorizing access to tools and resources. I spent way too long wiring up OAuth to build a ChatGPT connector and decided to wrap the pain into a library: @mcpauth/auth. What it is: - Self-hosted OAuth server, built for Model-Context-Protocol - Single authenticateUser() hook, so you plug in whatever session logic you already have (NextAuth, Auth.js, plain cookies, JWT, you name it) - Adapters: Next.js and Express; stores: Drizzle or Prisma - ISC licensed, no calls home Quick start is ~5 min: npm i @mcpauth/auth, add two route handlers, set three env vars. Live demo: https://mcpauth-nextjs.vercel.app/ If you’re building an MCP server or any internal AI tool that needs proper OAuth, I’d love feedback — especially on edge cases or new functionality. Repo: https://github.com/mcpauth/mcpauth

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, user · Missing: mac, agents, macos
80%80% 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
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host, users, calls · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% 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: users, way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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