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OpenGlad

OpenGlad

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github.com/TuguberkBusiness & Marketing9Updated 27w ago

About OpenGlad

OpenGlad is an MCP server for founders and AI agents that evaluates startup ideas and operations before resources are committed. It combines behavioral risk checks with market signals from Reddit, Hacker News, GitHub, and Polymarket.

Key features

  1. 01Startup idea loss-prevention pipeline
  2. 02Behavioral risk pattern scans
  3. 03Three-scenario loss simulations
  4. 04Multi-source market trend analysis
  5. 05Ranked comparison of 2–3 ideas
  6. 06Startup health diagnostics

Use cases

  1. 01Screening an idea before development
  2. 02Comparing several startup concepts
  3. 03Checking market saturation and entry risks
  4. 04Reviewing revenue, execution, or burnout risks

MCP support

OpenGlad exposes startup analysis tools, prompts, and a usage-guide resource to MCP clients. It uses Streamable HTTP and is available at the hosted MCP endpoint.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"openGlad": {
"command": "npx",
"args": [
"-y",
"mcp-remote"
]
}
}
}
Run commandnpx -y mcp-remote

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmmcp-remote

Tools

13

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
idea_descriptionstringYesThe user's startup idea or business concept in their own words. Pass the full description, not a summary.

Details

From the project README

openGlad Logo

The Loss-Prevention Friction Engine for Founders

An AI-powered MCP server that stops you from building things nobody wants using clinical analytics, behavioral pattern scanning, and real-time market intelligence from Reddit, Hacker News, GitHub, and Polymarket.

Tools • Quickstart • Architecture • Deployment

openGlad Demo


What is openGlad?

openGlad is a Model Context Protocol (MCP) server that acts as the ultimate friction engine for startups. It provides AI agents (Claude, Cursor, Windsurf, Le Chat, etc.) with specialized tools to enforce loss-prevention before you write a single line of code:

  • 🛑 Loss-Prevention Pipeline — Runs behavioral pattern scans, 3-scenario failure predictions, and locks building until monetization is confirmed.
  • 🔍 Multi-Source Market Intelligence — Aggregates real-time data from Reddit (11+ subreddits), Hacker News, GitHub, and Polymarket prediction markets to detect overcrowding and entry risks.
  • ⚔️ Comparative Friction Analysis — Runs parallel market intelligence on 2-3 ideas simultaneously and returns a ranked verdict on which one (if any) is worth pursuing.
  • 📊 Startup Diagnostics — Evaluates execution stability, revenue health, burnout risk, and distribution discipline.
  • 🩺 Clinical Triage — Objective, data-driven assessments with zero motivational fluff.

Think of it as an anti-delusion engine for your startup — designed to tell you 'no' before you waste months building the wrong thing.

Architecture

┌──────────────┐         ┌───────────────────────────────┐
│   AI Client  │  MCP    │   openGlad Worker             │
│  (Claude,    │◄──────►│   (Cloudflare Edge)           │
│   Cursor,    │         │      Version 5.0              │
│   Windsurf)  │         └─────────────┬─────────────────┘
└──────────────┘                       │ Parallel fetch (cached 1hr)
                          ┌────────────┼────────────┐
                   ┌──────▼──────┐ ┌──▼────┐ ┌─────▼──────┐ ┌──────▼──────┐
                   │   Reddit    │ │  HN   │ │   GitHub   │ │ Polymarket  │
                   │ 11+ subs    │ │Algolia│ │ Public API │ │  Gamma API  │
                   │ + topic exp.│ │ free  │ │  no key    │ │   free      │
                   └─────────────┘ └───────┘ └────────────┘ └─────────────┘

Tech Stack:

  • Runtime: Cloudflare Workers (edge-deployed, globally distributed)
  • Protocol: MCP (Model Context Protocol) via Streamable HTTP
  • Market Data: Reddit + Hacker News (Algolia) + GitHub + Polymarket (all free, no API keys)
  • Language: TypeScript (Modular Architecture)

Tools

🚧 Friction Engine (Loss Prevention)
ToolDescriptionMarket Data
run_the_betMega-pipeline combining Pattern Scan, Loss Simulation, and Revenue Gate. Start here for new ideas.Reddit + HN + GitHub + Polymarket
pattern_scanDetects behavioral risk patterns (overbuilding drift, monetization avoidance, prestige bias).None
loss_simulationGenerates 3-scenario failure predictions (best, likely, worst) with quantified expected loss.Reddit + HN + GitHub + Polymarket
revenue_gateLocks building until clear monetization strategy is confirmed. Produces unlock tasks.None
compare_ideasParallel multi-source analysis of 2-3 ideas with ranked comparison and single verdict.Reddit + HN + GitHub + Polymarket
🔍 Market Intelligence (Multi-Source)
ToolDescriptionMarket Data
analyze_market_trendsOvercrowding & entry risk filter. Detects tarpit ideas and late entry risks.Reddit + HN + GitHub + Polymarket
scan_reddit_trendsBroad trend scanner: sentiment, red flags, cautionary tales, and 6-12 month predictions.Reddit + HN + GitHub + Polymarket

Data Sources:

SourceAPIWhat it adds
RedditPublic JSON (free, no key)Community sentiment, 11 base subreddits + dynamic topic expansion
Hacker NewsAlgolia API (free, no key)Technical founder signal — developer adoption, HN discussions
GitHubPublic Search API (free, no key)Competitor repo activity, star velocity, open source adoption
PolymarketGamma API (free, no key)Prediction market odds — real money bets on outcome probabilities

Reddit subreddits (base): r/Startup_Ideas · r/Business_Ideas · r/SaaS · r/SideProject · r/EntrepreneurRideAlong · r/IndieHackers · r/Futurology · r/Technology · r/AINewsAndTrends · r/Startups · r/Entrepreneur

Dynamic expansion adds topic-specific subreddits (e.g. r/MachineLearning for AI queries, r/CryptoCurrency for crypto, r/fintech for finance).

🩺 Startup Diagnostics
ToolDescription
analyze_startupSmart triage router. Auto-detects ideas vs metrics and routes accordingly.
analyze_execution_stabilityAssesses development velocity, engineering risks, and technical debt.
analyze_revenue_healthEvaluates MRR/ARR trajectory, financial risks, churn, and unit economics.
analyze_burnout_riskDetects burnout signals from work patterns, cognitive load, and focus entropy.
analyze_distribution_disciplineMeasures marketing risks, output consistency, and funnel efficiency.
generate_full_diagnosisComprehensive system scan across all diagnostic dimensions.
💬 MCP Prompts
PromptDescription
run-the-betFull loss-prevention pipeline for a new idea.
market-checkMarket saturation and trend analysis combining broad scan + focused analysis.
should-i-buildQuick friction check: pattern scan + revenue gate to determine if building is allowed.
analyze-startupGuided startup analysis — triage and routing for ideas or metrics.
📖 MCP Resources
ResourceURIDescription
Usage Guideopenglad://guideAgent-readable guide with tool selection logic, recommended workflows, and usage tips.

Quickstart

Connect to the hosted server

The MCP server is deployed and ready to use:

https://openglad.tuguberk.dev/mcp
Claude Desktop / Cursor / Windsurf / Any MCP Client

Add to your MCP client configuration:

{
  "mcpServers": {
    "openGlad": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://openglad.tuguberk.dev/mcp"]
    }
  }
}
MCP Inspector (for testing)
npx @modelcontextprotocol/inspector@latest
# Enter URL: https://openglad.tuguberk.dev/mcp
Example Prompts

Once connected, try these with your AI client:

"Run the bet on my startup idea: an AI-powered tool that generates investor pitch decks from a one-page brief"
"Compare these two ideas for me: (1) AI accounting SaaS for freelancers, (2) no-code internal tools builder"
"Run a full health diagnostic on my startup with these metrics: MRR $12k, churn 8%, 3 developers, shipping weekly"
"Is the micro-SaaS market oversaturated? Check trends across Reddit, HN, and GitHub."

Deployment

Prerequisites
Deploy your own
# Clone and install
git clone https://github.com/tugberkakbulut/openGlad.git
cd openGlad
npm install

# Local development
npm run dev

# Deploy to Cloudflare
npx wrangler deploy

No API keys required — openGlad fetches all market data via free public APIs. Results are cached at the edge for 1 hour per query.

Project Structure
openGlad/
├── src/
│   ├── config/
│   │   └── constants.ts       # Subreddits + dynamic topic expansion map
│   ├── prompts/
│   │   └── index.ts           # LLM system prompts for all tools
│   ├── services/
│   │   ├── aggregator.ts      # Multi-source fetcher + evidence envelope wrapper
│   │   ├── reddit.ts          # Reddit search + engagement ranking + dedup + retry
│   │   ├── hackernews.ts      # HN Algolia API integration
│   │   ├── polymarket.ts      # Polymarket Gamma API integration
│   │   └── github.ts          # GitHub public search API integration
│   ├── tools/
│   │   ├── friction.ts        # Friction engine tools (run_the_bet, compare_ideas, etc.)
│   │   └── diagnostics.ts     # Diagnostic tools (execution, revenue, burnout, distribution)
│   ├── utils/
│   │   ├── dedupe.ts          # Jaccard N-gram deduplication + per-author caps + engagement scoring
│   │   └── helpers.ts         # Evidence envelope response builder
│   └── index.ts               # Server entry point, prompts, resources & tool registration
├── wrangler.jsonc              # Cloudflare Worker configuration
├── package.json
└── tsconfig.json

How It Works

Friction Engine Flow (Loss-Prevention)
  1. User asks → "I want to build an AI resume builder"
  2. AI client → Calls run_the_bet or analyze_startup
  3. openGlad Worker → Fetches from 4 sources in parallel: Reddit (11+ subreddits), HackerNews, GitHub, Polymarket (all cached 1hr at edge)
  4. Deduplication & Ranking → Jaccard similarity removes cross-source duplicates; per-author cap (max 3) prevents single-voice dominance; engagement scoring weights freshness + score + activity
  5. Pattern Scan → Identifies behavioral risks (overbuilding, monetization avoidance)
  6. Loss Simulation → Maps out 3 failure scenarios with quantified expected loss grounded in real market signals
  7. Revenue Gate → Locks building until monetization is proven
  8. User receives → A brutal reality check: blind spots, failure modes, and whether they're allowed to build
compare_ideas Flow
  1. User provides → 2-3 startup idea descriptions
  2. Parallel fetch → Market context fetched for all ideas simultaneously
  3. Comparative analysis → Each idea gets a compressed friction block (pattern risk, market signal, expected loss, gate status)
  4. Ranked verdict → Single recommendation on which idea (if any) to pursue

Built With

Inspiration

Multi-source aggregation, Jaccard deduplication, engagement-based ranking, thin-source retry, and evidence envelope patterns are inspired by mvanhorn/last30days-skill (MIT).

License

MIT

Comments

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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.