idea-reality-mcp 0.3.2

Pre-build reality check for AI coding agents. Stop building what already exists.

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idea-reality-mcp

We search. They guess.

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

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.

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 · [e-mail]