Upjack – Declarative framework for building apps over MCP

Hacker News

Open-sourced Upjack today. I built three apps with it: a CRM, a research assistant, and a todo app. Each one started the same way. I told Claude Code what I wanted. The app-builder skill generated schemas, domain skills in Markdown, a server, seed data. I pointed Claude Desktop at it. Working app, running locally. The CRM has five entities (contact, company, deal, pipeline, activity) and three skills (lead qualification, deal forecasting, follow-ups). Hooks fire on events (new contact -> auto-score). Scheduled jobs run on cron (pipeline review weekdays 9 AM). Named views filter data (hot leads: score >= 80). The research assistant has four entities (topic, source, note, report) and a synthesis skill that describes how to evaluate sources and cross-reference findings. Completely different domain, same framework. Apps are MCPB bundles. They run in Claude Desktop, Claude Code, Codex, or any MCP client. Any data app you can describe, you can build. The idea: we've always defined data models for developers to code against. LLMs don't need that. They reason over JSON Schema directly. They read Markdown and apply it. Give them a well-defined schema and domain rules, they do the rest. My sales lead wrote the CRM's qualification rubric. Not a developer. C-suite: +25 pts, corporate email: +10. The agent reads it and follows it. Storage is flat JSON files, one per record, backed by git. Grep your CRM, diff a contact, branch your pipeline. The storage layer is pluggable. Flat files today, other backends as the project grows. Tools are generated from schemas, six per entity. Built on FastMCP in Python, TypeScript library too. The server: from upjack.server import create_server mcp = create_server("manifest.json") mcp.run() Three tiers: (1) schemas + skills, no code. (2) create_server() one-liner. (3) Python/TypeScript escape hatch for custom logic. Looking for developers and businesses who want to experiment with AI-native applications. If you're interested in collaborating, open an issue or ping me directly. https://github.com/NimbleBrainInc/upjack Docs: https://upjack.dev

Visit product View on Hacker NewsFeb 25, 2026

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

97%97% 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: started · Missing: supports, reddit linkedin, podcasting

67%67% 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: lua, ide, pipe · Missing: https docs, excited, just released

49%49% 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: apps, way · Missing: mobile apps, ios, personal

45%45% 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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

23%23% predicted probability of success on AppSumo, 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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