MC

MCP server that checks if your project idea exists

Hacker News

MCP server that checks if your project idea exists

I built an MCP server that reality-checks project ideas against GitHub, Hacker News, npm, PyPI, and Product Hunt before you start coding. v0.3 adds a 3-stage keyword extraction pipeline: - Stage A: Stopword filtering (removes noise like "a tool that can") - Stage B: Intent anchoring (maps vague terms to searchable keywords) - Stage C: Synonym expansion + Chinese-to-English term mapping (150+ terms across 15 domains) Before: Chinese input "寵物預約看診 app" → searched raw Chinese → garbage results After: Same input → "pet appointment veterinary booking app" → relevant results Other stats: - 204 tests, 93% keyword extraction accuracy - Quick mode (GitHub + HN) ~2s, Deep mode (5 sources parallel) ~3-4s - Zero config: `uvx idea-reality-mcp` - Works with Claude Desktop, Cursor, any MCP client Built with Python + FastMCP + httpx async. MIT licensed. GitHub: https://github.com/mnemox-ai/idea-reality-mcp

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, mcp · Missing: mac, agents, macos
89%89% 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
31%31% 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
27%27% 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, hacker news, ide · Missing: https docs, excited, just released
24%24% 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
18%18% 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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