Hahooh – Give AI agents the power to build their own MCP tools

Hacker News

I built Hahooh because I was tired of writing the same boilerplate every time I wanted to give Claude or a local agent access to a new database schema or a random REST API. While there are many great MCP servers out there, they are often fragmented or require manual setup. I wanted to create a "WordPress for MCP" experience where: 1. Non-authed/Public tools: You can quickly bridge a public API to an agent. 2. Agent-Centric CLI: Instead of a human configuring everything, the CLI is designed so an agent can eventually say "I need access to this DB" and (with permission) create its own bridge. 3. Security: I’m using GCP Secret Manager for credentials and bcrypt for API key hashing. The stack is Go (backend), Vue 3 (frontend), and a Go-based CLI. I’m really interested in the "agent-as-builder" paradigm. If agents can build their own tools, the engineering bottleneck for AI workflows disappears. I'd love to hear your thoughts on the security model and how you’re handling MCP server management. Site: https://hahooh.xyz CLI: https://hahooh.xyz/download

Visit product View on Hacker NewsApr 28, 2026

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

96%96% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive

33%33% 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.

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

19%19% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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