openGlad

openGlad

openGlad is an MCP server that provides AI agents with tools for loss-prevention, market intelligence, and startup diagnostics using data from Reddit, Hacker News, GitHub, and Polymarket.

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README

<p align="center"> <img src="github/opengladlogo.png" alt="openGlad Logo" width="250" /> <p align="center"><strong>The Loss-Prevention Friction Engine for Founders</strong></p> <p align="center"> 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. </p> <p align="center"> <a href="#tools">Tools</a> β€’ <a href="#quickstart">Quickstart</a> β€’ <a href="#architecture">Architecture</a> β€’ <a href="#deployment">Deployment</a> </p> </p>

<p align="center"> <img src="github/openGladDemo.gif" alt="openGlad Demo" style="max-width: 100%;" /> </p>


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:

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

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

Architecture

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”         β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   AI Client  β”‚  MCP    β”‚   openGlad Worker             β”‚
β”‚  (Claude,    │◄──────►│   (Cloudflare Edge)           β”‚
β”‚   Cursor,    β”‚         β”‚      Version 5.0              β”‚
β”‚   Windsurf)  β”‚         β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜                       β”‚ Parallel fetch (cached 1hr)
                          β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                   β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β–Όβ”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”
                   β”‚   Reddit    β”‚ β”‚  HN   β”‚ β”‚   GitHub   β”‚ β”‚ Polymarket  β”‚
                   β”‚ 11+ subs    β”‚ β”‚Algoliaβ”‚ β”‚ Public API β”‚ β”‚  Gamma API  β”‚
                   β”‚ + topic exp.β”‚ β”‚ free  β”‚ β”‚  no key    β”‚ β”‚   free      β”‚
                   β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

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)

Tool Description Market Data
run_the_bet Mega-pipeline combining Pattern Scan, Loss Simulation, and Revenue Gate. Start here for new ideas. Reddit + HN + GitHub + Polymarket
pattern_scan Detects behavioral risk patterns (overbuilding drift, monetization avoidance, prestige bias). None
loss_simulation Generates 3-scenario failure predictions (best, likely, worst) with quantified expected loss. Reddit + HN + GitHub + Polymarket
revenue_gate Locks building until clear monetization strategy is confirmed. Produces unlock tasks. None
compare_ideas Parallel multi-source analysis of 2-3 ideas with ranked comparison and single verdict. Reddit + HN + GitHub + Polymarket

πŸ” Market Intelligence (Multi-Source)

Tool Description Market Data
analyze_market_trends Overcrowding & entry risk filter. Detects tarpit ideas and late entry risks. Reddit + HN + GitHub + Polymarket
scan_reddit_trends Broad trend scanner: sentiment, red flags, cautionary tales, and 6-12 month predictions. Reddit + HN + GitHub + Polymarket

Data Sources:

Source API What it adds
Reddit Public JSON (free, no key) Community sentiment, 11 base subreddits + dynamic topic expansion
Hacker News Algolia API (free, no key) Technical founder signal β€” developer adoption, HN discussions
GitHub Public Search API (free, no key) Competitor repo activity, star velocity, open source adoption
Polymarket Gamma API (free, no key) Prediction market odds β€” real money bets on outcome probabilities

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

Tool Description
analyze_startup Smart triage router. Auto-detects ideas vs metrics and routes accordingly.
analyze_execution_stability Assesses development velocity, engineering risks, and technical debt.
analyze_revenue_health Evaluates MRR/ARR trajectory, financial risks, churn, and unit economics.
analyze_burnout_risk Detects burnout signals from work patterns, cognitive load, and focus entropy.
analyze_distribution_discipline Measures marketing risks, output consistency, and funnel efficiency.
generate_full_diagnosis Comprehensive system scan across all diagnostic dimensions.

πŸ’¬ MCP Prompts

Prompt Description
run-the-bet Full loss-prevention pipeline for a new idea.
market-check Market saturation and trend analysis combining broad scan + focused analysis.
should-i-build Quick friction check: pattern scan + revenue gate to determine if building is allowed.
analyze-startup Guided startup analysis β€” triage and routing for ideas or metrics.

πŸ“– MCP Resources

Resource URI Description
Usage Guide openglad://guide Agent-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 β€” openGlad fetches all market data via free public APIs. Results are cached at the edge for 1 hour per query.

Project Structure

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

How It Works

Friction Engine Flow (Loss-Prevention)

  1. User asks β†’ "I want to build an AI resume builder"
  2. AI client β†’ Calls run_the_bet or analyze_startup
  3. openGlad Worker β†’ Fetches from 4 sources in parallel: Reddit (11+ subreddits), HackerNews, GitHub, Polymarket (all cached 1hr at edge)
  4. Deduplication & Ranking β†’ Jaccard similarity removes cross-source duplicates; per-author cap (max 3) prevents single-voice dominance; engagement scoring weights freshness + score + activity
  5. Pattern Scan β†’ Identifies behavioral risks (overbuilding, monetization avoidance)
  6. Loss Simulation β†’ Maps out 3 failure scenarios with quantified expected loss grounded in real market signals
  7. Revenue Gate β†’ Locks building until monetization is proven
  8. User receives β†’ A brutal reality check: blind spots, failure modes, and whether they're allowed to build

compare_ideas Flow

  1. User provides β†’ 2-3 startup idea descriptions
  2. Parallel fetch β†’ Market context fetched for all ideas simultaneously
  3. Comparative analysis β†’ Each idea gets a compressed friction block (pattern risk, market signal, expected loss, gate status)
  4. Ranked verdict β†’ 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

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