idea-reality-mcp
idea-reality-mcp is an MCP server that automatically checks if a software/startup idea already exists by scanning multiple data sources and returning a competitive analysis with a reality score.
Idea validation: Submit a natural-language description of your idea to determine if similar products or projects already exist, including a qualitative duplicate likelihood rating.
Reality scoring: Get a 0–100 "reality signal" score indicating market saturation, plus trend direction (accelerating/stable/declining) and a market momentum sub-score.
Two scan modes: Use quick mode (GitHub + Hacker News, <3 seconds) for a fast sanity check or deep mode (all 6 sources) for comprehensive analysis.
Multi-source evidence: Scans GitHub, Hacker News, npm, PyPI, Product Hunt, and Stack Overflow in parallel, providing detailed evidence (repo counts, star counts, package counts, discussion mentions, etc.).
Competitor discovery: Identifies top similar projects with names, URLs, star counts, and descriptions.
Pivot suggestions: Provides AI-generated recommendations for differentiators or pivots when high competition is detected.
Flexible integration: Usable via MCP tool calls (for AI agents like Claude/Cursor), REST API, or Python client — no MCP setup required for the latter two.
CI/CD support: Integrates with GitHub Actions to automatically validate feature proposals in pull requests.
Setup & health tools: Includes CLI commands for guided configuration and server health checks.
Searches GitHub repositories and star counts to identify existing open-source projects and assess competition for a given idea.
Scans the npm registry for existing packages and tools to determine if a JavaScript-related idea has already been implemented.
Searches for launched products on Product Hunt to identify existing competitors and market entries.
Scans the Python Package Index to find existing Python libraries and tools matching a project description.
English | 繁體中文
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.
How it works
Describe your idea in plain English — e.g. "a CLI tool that converts Figma designs to React components"
idea_check scans 5 databases in parallel (GitHub repos + stars, Hacker News discussions, npm/PyPI packages, Stack Overflow questions)
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 fastOne 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 Code3. 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 claudeSetup & Configuration
First-time guided setup:
idea-reality setupThis walks you through:
Terms acceptance — data collection policy and disclaimer
Platform detection — auto-detects Claude Desktop, Claude Code, Cursor, Windsurf, Cline
Config generation — prints the exact JSON snippet for your platform
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 JSONSupported: 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 APIUsage
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"]) # 0-100Free. 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.
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 |
| string | yes | Natural-language description of idea |
|
| no |
|
{
"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 limitsPRODUCTHUNT_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
Found a blind spot?
If the tool missed obvious competitors or returned irrelevant results:
Open an issue with your idea text and the output
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 toolidea_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.
| Name | Required | Description | Default |
|---|---|---|---|
| idea_text | Yes | ||
| depth | No | quick |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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
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.
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.
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.
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
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Search GitHub, npm, PyPI, StackOverflow, ArXiv from one MCP — built for coding agents.
Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.
Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.
Real-time fact-check, citation verification, and source-freshness for AI agents.
Related MCP Servers
- AlicenseAqualityCmaintenanceEnables AI agents to prospect across GitHub, PyPI, and npm, searching repositories, packages, and code patterns with read-only tools.73MIT
- AlicenseAqualityDmaintenancePre-build reality check for AI coding agents — searches 5 real databases (GitHub, Hacker News, npm, PyPI, Product Hunt) to check whether an idea already exists before you build it.1MIT
- FlicenseNot gradedqualityCmaintenanceProvides AI agents with 6 tools for searching Hugging Face models, GitHub trending repos, analyzing GitHub repositories, fetching dev.to articles, Show HN launches, and Product Hunt daily launches. Built for dev tooling research and AI ecosystem analysis.
- AlicenseNot gradedqualityFmaintenanceOpen source intelligence for AI agents — GitHub project-health scoring, package dependency-risk analysis, trending repositories, license checks, and side-by-side package comparison.MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/mnemox-ai/idea-reality-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server