mnemox-ai

idea-reality-mcp

Community mnemox-ai
Updated

Pre-build reality check for AI coding agents. Scans GitHub, HN, npm, PyPI, Product Hunt. MCP server. 290+ stars.

English | 繁體中文

idea-reality-mcp

We search. They guess.

The only idea validator that searches real data. 5 sources. Quantified signal. Zero hallucination.

License: MITPython 3.11+MCPPyPISmitheryGitHub stars

The problem

Every developer has wasted days building something that already exists with 5,000 stars on GitHub.

You ask ChatGPT: "Is there already a tool that does X?"

ChatGPT says: "That's a great idea! There are some similar tools, but you can definitely build something better!"

That's not validation. That's cheerleading.

What we do instead

You: "AI code review tool"

idea-reality-mcp:
├── reality_signal: 90/100
├── GitHub repos: 847
├── Top competitor: reviewdog (9,094 ⭐)
├── npm packages: 56
├── HN discussions: 254
└── Verdict: HIGH — consider pivoting to a niche

One gives you encouragement. The other gives you facts.

Which one do you trust your next 3 months on?

Try it now (30 seconds)

uvx idea-reality-mcp

Or try it in your browser — no install required.

Why not just ask ChatGPT?

idea-reality-mcp ChatGPT / ValidatorAI / IdeaProof
Data source GitHub + HN + npm + PyPI + Product Hunt (real-time) LLM generation (no real source searched)
Output Score 0-100 + real projects with star counts Text opinion ("Sounds promising!")
Verifiable Every number has a source Not verifiable
Integration MCP / CLI / API / Web Web-only
Price Free, open-source, forever Free trial → paywall
Audience Developers (before writing code) Non-technical founders (before writing pitch decks)

TL;DR — We search 5 real databases. They generate opinions.

New: AI-powered search intelligence

Claude Haiku 4.5 now generates optimal search queries from your idea description — in any language — with automatic fallback to our battle-tested dictionary pipeline.

Before Now
English ideas ✅ Good ✅ Good
Chinese / non-English ideas ⚠️ Dictionary lookup (150+ terms) ✅ Native understanding
Ambiguous descriptions ⚠️ Keyword matching ✅ Semantic extraction
Reliability 100% (no external API) 100% (graceful fallback to dictionary)

The LLM understands your idea. The dictionary is your safety net. You always get results.

Make your AI agent check automatically

Add this to your .claude/instructions.md or CLAUDE.md:

Before building any new tool, feature, or project,
always run idea_check first to verify the idea hasn't been built already.

This is the highest-ROI setup. Your AI agent will validate ideas before writing a single line of code. Install once, protect every project.

More templates: Claude Code · Cursor · Windsurf · Copilot — copy-paste snippets for every AI coding tool.

Install (5 minutes)

Claude Desktop

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "idea-reality": {
      "command": "uvx",
      "args": ["idea-reality-mcp"]
    }
  }
}

Cursor

Paste into .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "idea-reality": {
      "command": "uvx",
      "args": ["idea-reality-mcp"]
    }
  }
}

Claude Code (CLI)

claude mcp add idea-reality -- uvx idea-reality-mcp

Smithery (Remote)

npx -y @smithery/cli install idea-reality-mcp --client claude

Optional: Environment variables

export GITHUB_TOKEN=ghp_...        # Higher GitHub API rate limits
export PRODUCTHUNT_TOKEN=your_...  # Enable Product Hunt (deep mode)

Usage

"I have a side project idea — should I build it?"

Tell your AI agent:

Before I start building, check if this already exists:
a CLI tool that converts Figma designs to React components

The agent calls idea_check and returns: reality_signal, top competitors, and pivot suggestions.

"Find competitors and alternatives"

idea_check("open source feature flag service", depth="deep")

Deep mode scans all 5 sources in parallel — GitHub repos, HN discussions, npm packages, PyPI packages, and Product Hunt — and returns ranked results.

"Build-or-buy sanity check before a sprint"

We're about to spend 2 weeks building an internal error tracking tool.
Run a reality check first.

If the signal comes back at 85+ with mature open-source alternatives, you just saved your team 2 weeks.

Tool schema

idea_check

Parameter Type Required Description
idea_text string yes Natural-language description of idea
depth "quick" | "deep" no "quick" = GitHub + HN (default). "deep" = all 5 sources in parallel

Output: reality_signal (0-100), duplicate_likelihood, evidence[], top_similars[], pivot_hints[], meta{}

Full output example
{
  "reality_signal": 72,
  "duplicate_likelihood": "high",
  "evidence": [
    {"source": "github", "type": "repo_count", "query": "...", "count": 342},
    {"source": "github", "type": "max_stars", "query": "...", "count": 15000},
    {"source": "hackernews", "type": "mention_count", "query": "...", "count": 18},
    {"source": "npm", "type": "package_count", "query": "...", "count": 56},
    {"source": "pypi", "type": "package_count", "query": "...", "count": 23},
    {"source": "producthunt", "type": "product_count", "query": "...", "count": 8}
  ],
  "top_similars": [
    {"name": "user/repo", "url": "https://github.com/...", "stars": 15000, "description": "..."}
  ],
  "pivot_hints": [
    "High competition. Consider a niche differentiator...",
    "The leading project may have gaps in...",
    "Consider building an integration or plugin..."
  ],
  "meta": {
    "sources_used": ["github", "hackernews", "npm", "pypi", "producthunt"],
    "keyword_source": "llm",
    "depth": "deep",
    "version": "0.3.2"
  }
}

Scoring weights

Mode GitHub repos GitHub stars HN npm PyPI Product Hunt
Quick 60% 20% 20%
Deep 25% 10% 15% 20% 15% 15%

If Product Hunt is unavailable (no token), its weight is redistributed automatically.

CI: Auto-check on Pull Requests

Add .github/workflows/idea-check.yml to run reality checks when PRs propose new features:

name: Idea Reality Check
on:
  pull_request:
    paths: ['docs/proposals/**', 'RFC/**']

jobs:
  check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install idea-reality-mcp httpx
      - name: Run idea check
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          python -c "
          import asyncio, json
          from idea_reality_mcp.sources.github import search_github_repos
          from idea_reality_mcp.sources.hn import search_hn
          from idea_reality_mcp.scoring.engine import compute_signal, extract_keywords

          async def main():
              idea = open('docs/proposals/latest.md').read()[:500]
              kw = extract_keywords(idea)
              gh = await search_github_repos(kw)
              hn = await search_hn(kw)
              report = compute_signal(gh, hn)
              print(json.dumps(report, indent=2))

          asyncio.run(main())
          "
      - name: Comment on PR
        if: always()
        uses: actions/github-script@v7
        with:
          script: |
            github.rest.issues.createComment({
              owner: context.repo.owner,
              repo: context.repo.repo,
              issue_number: context.issue.number,
              body: '## Idea Reality Check\nSee workflow run for full report.'
            })

Roadmap

  • v0.1 — GitHub + HN search, basic scoring
  • v0.2 — Deep mode (npm, PyPI, Product Hunt), improved keyword extraction
  • v0.3 — 3-stage keyword pipeline, 150+ Chinese term mappings, synonym expansion, LLM-powered search (Render API)
  • v0.4 — Trend detection and timing analysis
  • v1.0 — Idea Memory Dataset (opt-in anonymous logging)

Found a blind spot?

If the tool missed obvious competitors or returned irrelevant results:

  1. Open an issue with your idea text and the output
  2. We'll improve the keyword extraction for your domain

License

MIT — see LICENSE

Contact

Built by Mnemox AI · [email protected]

MCP Server · Populars

MCP Server · New

    YV17labs

    ghostdesk

    Give any AI agent a full desktop — it sees the screen, clicks, types, and runs apps like a human. Automate anything with a UI: browsers, legacy software, internal tools. No API needed. One Docker command.

    Community YV17labs
    remotebrowser

    mcp

    Free your data

    Community remotebrowser
    Decodo

    Decodo MCP Server

    The Decodo MCP server which enables MCP clients to interface with services.

    Community Decodo
    kuberstar

    Qartez MCP

    Semantic code intelligence MCP server for Claude Code - project maps, symbol search, impact analysis, and more

    Community kuberstar
    aovestdipaperino

    tokensave

    Rust port of CodeGraph — a local-first code intelligence system that builds semantic knowledge graphs from codebases. Ported from the original TypeScript implementation by @colbymchenry.

    Community aovestdipaperino