Idea-reality-MCP – MCP server that searches real data before you build

Hacker News

I kept wasting hours building things that already existed. Asked Claude to help me build a food delivery app — searched GitHub afterward, found 847 similar repos with 12 competitors over 1k stars. 6 hours wasted. So I built an MCP server that does the searching before you write code. It scans 5 real-time sources (GitHub, Hacker News, npm, PyPI, Product Hunt) and returns a quantified reality_signal (0-100) with actual evidence — repo counts, star counts, top competitors, and pivot suggestions. Example: "AI code review tool" → reality_signal: 90, 847 repos, top competitor reviewdog (9,094 stars), 254 HN mentions. What it's NOT: not a business plan generator, not an LLM opinion wrapper. Every number comes from a real API call you can verify. - Quick mode: GitHub + HN (default) - Deep mode: all 5 sources in parallel - Works with Claude Desktop, Cursor, Claude Code - One-line install: uvx idea-reality-mcp - Also available as web demo (no install needed) GitHub: https://github.com/mnemox-ai/idea-reality-mcp Web demo: https://mnemox.ai/check MCP Registry: io.github.mnemox-ai/idea-reality-mcp Built with Python + FastMCP, 120 tests, published on PyPI. Happy to answer any questions about the scoring algorithm or MCP integration.

Visit product View on Hacker NewsFeb 27, 2026

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, mcp · 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

60%60% 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

32%32% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, code review, hacker news · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

21%21% 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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