Cloud-Deployable Bridge Between OpenAPI Endpoints and MCP

Hacker News

Hey folks, I've been building AI agents that need to talk to various APIs, and I got tired of writing custom integrations for every service. So I built the MCP-OpenAPI Server to solve this problem! It's a simple bridge that lets AI agents discover and use our existing OpenAPI endpoints through the Model Context Protocol. No need to write custom code for each service - just point it at the OpenAPI specs, choose which endpoints to expose, and you're good to go. What makes this different from other MCP servers is that it uses SSE transport instead of stdio, making it work well for multi-tenant cloud setups. It handles authentication forwarding and generates properly typed arguments based on your schema (handling nested types, arrays, etc). This can be useful for: - Server-side AI agent developers who want to easily integrate with third party SaaS tools like Stripe, Zendesk, etc. - API providers who want to expose an existing API to MCP - Anyone prototyping AI/API interactions before building a custom MCP server. I'd love to hear what you think! GitHub: https://github.com/conorbranagan/mcp-openapi

Visit product View on Hacker NewsMar 31, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

92%92% 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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

52%52% 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 · Missing: mobile apps, ios, personal

46%46% 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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, saas · Missing: mrr, revenue, profit

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.

Incorrect prediction on native model

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