Gin-MCP- All Gin APIs are now MCP-compatible without rewriting

Hacker News

(Just add two lines of code to integrate the library!) I built gin-mcp to automatically expose existing Gin web APIs as Model Context Protocol (MCP) tools, making them usable by clients like Cursor with minimal effort. It uses reflection on your *gin.Engine to discover endpoints and infers basic schemas for path and query parameters automatically. The idea is to be zero-configuration by default --- you add it to your existing Gin app, provide a BaseURL, mount the MCP handler (mcp.Mount("/mcp")), and your API becomes available as tools. For more complex scenarios where inference isn't enough (e.g., specific struct validation, request body definitions), you can explicitly define schemas using mcp.RegisterSchema(method, path, querySchema, bodySchema). You can also filter which endpoints are exposed. It aims to reduce the boilerplate needed to connect internal APIs to MCP-compatible AI tools, letting you leverage your existing backend services more easily. Check out the repo for code, examples, and usage details: https://github.com/ckanthony/gin-mcp Feedback is welcome!

Visit product View on Hacker NewsApr 17, 2025

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

87%87% 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 HackersIH features products with proven revenue · Strong signals: para, ios, compatible · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

44%44% 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: ios, para · Missing: mobile apps, personal, entrepreneurs

41%41% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% 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.

Correct prediction on native model

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