idea-reality-mcp
Prevents AI agents from generating redundant or architecturally misaligned code by validating ideas against existing solutions, libraries, and market precede…
Pre-build reality check for AI coding agents. Scans GitHub, HN, npm, PyPI, Product Hunt. MCP server. 290+ stars.
- Ask Claude to check if your startup idea already exists on GitHub or Product Hunt
- Generate feasibility assessment by scanning npm and PyPI for existing libraries
- Find competing solutions and similar projects before starting development
Prevents AI agents from generating redundant or architecturally misaligned code by validating ideas against existing solutions, libraries, and market precedent before implementation begins.
Engineering teams using AI-assisted coding who need to enforce architectural consistency and avoid reinventing existing, proven solutions.
https://github.com/mnemox-ai/idea-reality-mcp
By mnemox-ai
How to Get It
claude mcp add idea-reality-mcp -- npx -y idea-reality-mcp
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Check if my startup idea already exists on GitHub or Product Hunt
Trust Signals Auto-scanned
Data & Access
Community Pulse Emerging
Discussed on Hacker News
- Show HN: MCP server that checks if your project idea exists — Hacker News · 2 pts
- Show HN: Idea-reality-MCP – MCP server that searches real data before you build — Hacker News · 1 pts
- Show HN: Idea Reality MCP – Pre-build reality check for AI coding agents — Hacker News · 1 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 754 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 754 GitHub stars; contributors unknown; last commit 0d ago; license MIT.