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idea-reality-mcp

How to check if someone already built your app idea — automatically.

idea-reality-mcp is an MCP server that scans GitHub, npm, PyPI, Hacker News, and Stack Overflow to check if your startup idea already exists. It returns a 0–100 reality score with evidence, trend detection, and pivot suggestions — so your AI agent can decide whether to build, pivot, or kill the idea before writing any code.

When to use this: You're about to start a new project and want to know if similar tools already exist, how competitive the space is, and whether the market is growing or declining.

Project status (August 2026): Maintenance mode. The tool works, stays free & open source, and the hosted API remains up; bug reports are reviewed, but no new features are planned.

Not just checking — building it? After a reality check, open your idea as a public project on AngelRun — ship updates, climb the season, and get seen by angels.

PyPI Smithery License: MIT Tests GitHub stars Downloads

How it works

  1. Describe your idea in plain English — e.g. "a CLI tool that converts Figma designs to React components"

  2. idea_check scans 5 databases in parallel (GitHub repos + stars, Hacker News discussions, npm/PyPI packages, Stack Overflow questions)

  3. Get a 0–100 reality score with trend direction (accelerating/stable/declining), top competitors, and AI-generated pivot suggestions

Related MCP server: idea-reality-mcp

What you get

You: "AI code review tool"

idea_check →
├── reality_signal: 92/100
├── trend: accelerating ↗
├── market_momentum: 73/100
├── GitHub repos: 847 (45% created in last 6 months)
├── Top competitor: reviewdog (9,094 ⭐)
├── npm packages: 56
├── HN discussions: 254 (trending up)
└── Verdict: HIGH — market is accelerating, find a niche fast

One score. Six sources. Trend detection. Your agent decides what to do next.

Quick Start

# 1. Install
uvx idea-reality-mcp

# 2. Add to your agent
claude mcp add idea-reality -- uvx idea-reality-mcp   # Claude Code

3. Ask your agent: "Before I start building, check if this already exists: a CLI tool that converts Figma designs to React components"

That's it. The agent calls idea_check and returns: reality_signal, top competitors, and pivot suggestions.

Claude Desktop / Cursor — add to config JSON:

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

Config location: macOS ~/Library/Application Support/Claude/claude_desktop_config.json · Windows %APPDATA%\Claude\claude_desktop_config.json · Cursor .cursor/mcp.json

Smithery (remote, no local install):

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

Setup & Configuration

First-time guided setup:

idea-reality setup

This walks you through:

  1. Terms acceptance — data collection policy and disclaimer

  2. Platform detection — auto-detects Claude Desktop, Claude Code, Cursor, Windsurf, Cline

  3. Config generation — prints the exact JSON snippet for your platform

  4. Health check — verifies MCP server, tools, and scoring engine

Platform Configs

idea-reality config              # interactive menu
idea-reality config claude_code  # auto-installs via CLI
idea-reality config cursor       # prints Cursor config
idea-reality config raw_json     # generic MCP JSON

Supported: Claude Desktop · Claude Code · Cursor · Windsurf · Cline · Smithery · Docker

Health Check

idea-reality doctor        # core checks (~2s)
idea-reality doctor --full # + GitHub API, all 6 sources, Anthropic API

Usage

MCP tool call (any MCP-compatible agent):

{
  "tool": "idea_check",
  "arguments": {
    "idea_text": "a CLI tool that converts Figma designs to React components",
    "depth": "deep"
  }
}

REST API (no MCP required):

curl -X POST https://idea-reality-mcp.onrender.com/api/check \
  -H "Content-Type: application/json" \
  -d '{"idea_text": "AI code review tool", "depth": "quick"}'

Python:

import httpx

resp = httpx.post("https://idea-reality-mcp.onrender.com/api/check", json={
    "idea_text": "AI code review tool",
    "depth": "deep"
})
print(resp.json()["reality_signal"])  # 0-100

Free. No API key required.

Why not just Google it?

Your AI agent never Googles anything before it starts building. idea_check runs inside your agent — it triggers automatically whether you remember or not.

Google

ChatGPT

idea-reality-mcp

Who runs it

You, manually

You, manually

Your agent, automatically

Output

10 blue links

"Sounds promising!"

Score 0-100 + evidence

Sources

Web pages

None (LLM)

GitHub + HN + npm + PyPI + PH + SO

Price

Free

Paywall

Free & open-source (MIT)

Modes

Mode

Sources

Use case

quick (default)

GitHub + HN

Fast sanity check, < 3 seconds

deep

GitHub + HN + npm + PyPI + Stack Overflow

Full competitive scan

Source

Quick

Deep

GitHub repos

60%

22%

GitHub stars

20%

9%

Hacker News

20%

14%

npm

18%

PyPI

13%

Stack Overflow

10%

If a source is unavailable, its weight is redistributed automatically — so the deep-mode weights above are renormalised over the sources that actually answered.

Product Hunt was removed on 2026-07-17. It had carried 14% of the deep-mode weight since launch and had never returned a single result: the adapter asked for posts(search: $query), and Product Hunt's API has no text search on posts at all (Field 'posts' doesn't accept argument 'search'). Its weight is now redistributed to sources that answer. If you need it back, it needs a real search surface — not a token.

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 6 sources

{
  "reality_signal": 72,
  "duplicate_likelihood": "high",
  "trend": "accelerating",
  "sub_scores": { "market_momentum": 73 },
  "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": "stackoverflow", "type": "question_count", "query": "...", "count": 120}
  ],
  "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..."
  ]
}

CI: Auto-check on Pull Requests

Use idea-check-action to validate feature proposals:

name: Idea Reality Check
on:
  issues:
    types: [opened]

jobs:
  check:
    if: contains(github.event.issue.labels.*.name, 'proposal')
    runs-on: ubuntu-latest
    steps:
      - uses: mnemox-ai/idea-check-action@v1
        with:
          idea: ${{ github.event.issue.title }}
          github-token: ${{ secrets.GITHUB_TOKEN }}

Optional config

export GITHUB_TOKEN=ghp_...        # Higher GitHub API rate limits

PRODUCTHUNT_TOKEN no longer does anything — the source is disabled and ignores it. Setting it used to be worse than useless: it un-skipped a source whose query the API rejects, so it reported "0 competitors on Product Hunt" into 14% of the deep score.

Auto-trigger: Add one line to your CLAUDE.md, .cursorrules, or .github/copilot-instructions.md:

When starting a new project, use the idea_check MCP tool to check if similar projects already exist.

Roadmap

  • v0.1 — GitHub + HN search, basic scoring

  • v0.2 — Deep mode (npm, PyPI, Product Hunt), keyword extraction

  • v0.3 — 3-stage keyword pipeline, Chinese term mappings, LLM-powered search

  • v0.4 — Score History, Agent Templates, GitHub Action

  • v0.5 — Temporal signals, trend detection, market momentum

  • v0.6 — Onboarding CLI (idea-reality setup, config, doctor)

Star History

Star History Chart

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

Contributing

See CONTRIBUTING.md (繁體中文).

License

MIT — see LICENSE

Built by Mnemox AI · dev@mnemox.ai

Available Tools

1 tool
idea_checkA

Check if a product idea already exists before building it.

Use when users discuss new project ideas, ask about competition, market saturation, or whether something has been built before.

Trigger phrases: "has anyone built", "does this exist", "check competition", "is this idea original", "有沒有人做過", "市場上有類似的嗎", "幫我查這個點子"

Args: idea_text: Natural-language description of the idea. depth: "quick" (GitHub + HN, fast) or "deep" (all sources in parallel).

Returns: Reality check report with signal score, evidence, similar projects, and pivot hints.

ParametersJSON Schema
NameRequiredDescriptionDefault
idea_textYes
depthNoquick

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (checks for existing ideas), mentions sources (GitHub + HN for 'quick', all sources for 'deep'), and outlines the return format (reality check report with specific components). It doesn't mention rate limits, authentication needs, or error handling, but covers the core behavior well for a tool without annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose. Each sentence earns its place: the first states what it does, the second provides usage guidelines, the third lists trigger phrases, and the last sections explain parameters and returns. There's no wasted text, and it's appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 parameters (1 required), 0% schema description coverage, no annotations, but has an output schema, the description provides excellent contextual completeness. It explains the tool's purpose, when to use it, parameters, and return format. The output schema existence means the description doesn't need to detail return values, and it appropriately focuses on semantics and usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate fully. It provides clear semantic explanations for both parameters: 'idea_text' is described as 'Natural-language description of the idea' and 'depth' is explained with its two enum values ('quick' uses GitHub+HN, fast; 'deep' uses all sources in parallel). This adds significant value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Check if a product idea already exists') and resource ('product idea'). It distinguishes the tool's function from potential alternatives by specifying it's for validation before building. The title is null, so the description carries the full burden and does so effectively.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use the tool: 'when users discuss new project ideas, ask about competition, market saturation, or whether something has been built before.' It includes specific trigger phrases in multiple languages, making it very clear about the appropriate context for invocation. No sibling tools exist, so differentiation isn't needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

TDQS

A4.4/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clearly defined purpose: checking if a product idea already exists. No other tools exist that could be confused with it.

Naming Consistency5/5

The tool name 'idea_check' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect as there are no other names to compare against or deviate from any pattern.

Tool Count2/5

A single tool is too few for a server named 'idea-reality-mcp', which suggests a broader scope related to idea validation or market research. While the tool is well-described, the server feels thin and incomplete with just one operation, limiting its utility for agents handling complex idea evaluation workflows.

Completeness2/5

The server is severely incomplete for its implied domain of idea reality checking. It only offers a check operation but lacks tools for related tasks like analyzing market trends, comparing features, tracking idea evolution, or managing a portfolio of ideas. This creates significant gaps that will hinder agents from performing comprehensive idea assessment.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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