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MCP Generator for Your API

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MCP Generator for Your API

Hey HN, Sagiv from liblab is here. We've been deep in the world of API tooling for years at liblab, primarily focusing on generating SDKs and documentation from OpenAPI specs. Recently, we kept running into a recurring frustration: connecting AI tools to existing APIs is way more complex than it should be. You start with a simple goal – let an LLM talk to your API – and suddenly you're neck-deep in spinning up infrastructure, wrestling with authentication, and writing a ton of custom glue code just to translate natural language into structured API calls. Then you have to maintain it all. To scratch our own itch, we built the MCP Generator. It takes your API specification and generates a fully functional MCP (Model Context Protocol) server that's deployed to the cloud and ready to use in about 30 seconds. You can also download the server for free local usage. The idea is to cut out all the boilerplate and manual integration work. You just drop the generated URL into your AI tool, and you can start interacting with your API through natural language right away. This builds on our existing SDK code generation engine, but extends it to support the MCP protocol. This means that as your API evolves, your MCP server can be regenerated to stay in sync without you needing to touch a thing. We've seen some interesting early uses, and thought the HN community might have some cool ideas too: - Building devtools that allow LLMs to perform actions against your internal or external APIs. - Querying internal metrics or services using natural language questions. - Surfacing structured documentation and content through conversational AI interfaces. - Speaking with your API service instead of coding functions. We just launched and would genuinely love to get your feedback, thoughts, and even your most challenging edge cases. We're here to dive into the technical details and answer any questions. You can try it out here: https://mcp.liblab.com/ Looking forward to hearing what you think!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, context · Missing: mac, agents, macos
91%91% 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
75%75% 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
67%67% 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: way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, calls · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · 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 · 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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