Chomptron
MCP serverEverything elseGenerate a recipe from ingredients on hand, with optional dietary restrictions. Free.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use Chomptron
Install Chomptron
The server’s own address, for the clients that take one directly. Or connect ahel onceand every client you use reads it from one address, with the account kept on ahel rather than in each client’s config.
Claude Code
claude mcp add --transport http chomptron 'https://chomptron.com/mcp'Run it once in your project, then open /mcp to approve any sign-in the server asks for.
Claude Desktop
https://chomptron.com/mcpAdd a custom connector in Settings, paste this address, and approve the sign-in.
Cursor
cursor://anysphere.cursor-deeplink/mcp/install?name=chomptron&config=eyJ1cmwiOiJodHRwczovL2Nob21wdHJvbi5jb20vbWNwIn0=Open the link and Cursor adds the server at that address.
ChatGPT
https://chomptron.com/mcpIn Settings, enable Developer mode, create an MCP app, and paste this address. Your plan and workspace must allow custom apps.
Codex
codex mcp add chomptron --url 'https://chomptron.com/mcp'Run it once, then sign in with codex mcp login chomptron if the server asks for an account.
From the project's README
As published by swantron/chomptron in README.md.
AI-powered recipe generator that transforms ingredients into delicious recipes using Google Gemini AI. Deployed on Google Cloud Run at chomptron.com.
What It Does
Enter ingredients you have in your kitchen, and Chomptron generates creative, practical recipes complete with measurements, instructions, cooking time, and serving sizes.
Local Development
npm install
export GEMINI_API_KEY="your-api-key-here"
export GEMINI_MODEL="gemini-3.1-flash-lite" # Optional, defaults to gemini-3.1-flash-lite
npm start
Visit http://localhost:8080
Get API key: https://makersuite.google.com/app/apikey
Environment Variables
GEMINI_API_KEY(required) - Your Google Gemini API keyGEMINI_MODEL(optional) - Model to use, defaults togemini-3.1-flash-litePORT(optional) - Server port, defaults to 8080
Model Configuration
The Gemini model can be configured via the GEMINI_MODEL environment variable:
Recommended Models (as of December 2025):
gemini-3.1-flash-lite(default) - Best free tier limits: 15 RPM, 1,000 RPDgemini-2.5-flash- 10 RPM, 250 RPDgemini-2.0-flash- 10 RPM, 200 RPD (⚠️ unstable quota, often showslimit: 0)gemini-1.5-flash- Legacy model, may have better limits than 2.0
December 2025 Quota Shift:
Google overhauled free tier quotas in December 2025:
gemini-2.0-flashwas removed from fully unauthenticated free tier- Many accounts see
limit: 0errors for newer models without billing enabled - Free tier quotas don't automatically reset monthly
Fixing "Limit: 0" Errors:
If you're seeing quota errors with limit: 0:
- Switch to
gemini-2.5-flash-lite- Best free tier model currently available - Enable billing (Pay-As-You-Go) - Linking a credit card (even if you don't spend) moves you from "Limited Free" to "Tier 1" and unlocks promised free quotas
- Check your region - EEA, UK, and Switzerland have restricted free tier access
- Monitor usage - Visit
/api/usageendpoint or https://ai.dev/usage
Free Tier Limits (as of Dec 2025):
| Model | Requests/Minute | Requests/Day | Best For |
|---|---|---|---|
| gemini-2.5-flash-lite | 15 | 1,000 | High-volume apps |
| gemini-2.5-flash | 10 | 250 | General use |
| gemini-2.0-flash | 10 | 200 | Legacy (unstable) |
| gemini-2.5-pro | 2 | 50 | Complex reasoning |
Testing
npm test
Tests validate:
- Modular File Structure: Verification of external
styles.cssandapp.jslinking - CI/CD & DevOps: Docker and Cloud Build configurations
- API Integrity: Health check endpoints (
/health,/ready) and recipe generation - Feature Robustness: Recipe history, scaling logic, and favorites
API Endpoints
-
GET /health- Liveness check, returns service status -
GET /ready- Readiness check, verifies AI configuration and shows current model -
GET /api/usage- Usage statistics (total requests, quota errors, model info) -
POST /api/generate-recipe- Main recipe generation endpoint- Request Body:
{ "ingredients": "chicken, tomatoes, garlic", "dietaryPreferences": { "vegan": false, "vegetarian": false, "glutenFree": false, "dairyFree": false, "nutFree": false, "shellfishFree": false, "eggFree": false, "soyFree": false } } - Response:
{ "success": true, "recipe": "**Recipe Name:** ...", "cached": false }
- Request Body:
-
POST /mcp— MCP Streamable HTTP. Toolgenerate_recipe(ingredients + optional dietary flags). Unauthenticated. Daily capMCP_DAILY_RECIPE_LIMIT(default 20), persisted toMCP_BUDGET_FILEso a process restart on the same instance does not reset it.
Architecture
Chomptron is built as a serverless application on Google Cloud Run for cost efficiency and automatic scaling.
Tech Stack:
- Backend: Node.js 24 + Express
- AI: Google Gemini (configurable model, defaults to gemini-3.1-flash-lite)
- Frontend: Modular Vanilla HTML/CSS/JavaScript (Clean separation of concerns)
- Storage: Browser localStorage for recipe history
- Platform: Google Cloud Run (serverless)
- CI/CD: Cloud Build
- Domain: chomptron.com
Why Serverless?
- Scales to zero when idle → $0 cost (vs. $5-50/month traditional hosting)
- Auto-scales from 0 to 1000+ instances based on traffic
- Zero maintenance - no servers to manage, patch, or configure
- Perfect for AI workloads - handles burst traffic and CPU-intensive recipe generation efficiently
Performance Optimizations:
- In-Memory Caching: Per-instance recipe cache (24-hour TTL, max 100 recipes)
- Structured Recipe Parsing: Extracts recipe components for better display and scaling
- Client-Side Rate Limiting: Prevents excessive API calls
- Smart Retry Logic: Handles quota errors gracefully with exponential backoff
Usage Guide
Generating Recipes
- Enter your ingredients in the text area
- (Optional) Select dietary preferences/allergies
- Click "Generate Recipe ✨"
- View your structured recipe with:
- Recipe name and metadata
- Scaled ingredients list
- Step-by-step instructions
- Cooking tips
Recipe Features
- Scale Servings: Use +/- buttons to adjust serving size (0.25x to 4x)
- Rate Recipes: Click stars to rate recipes (1-5 stars)
- Add Notes: Type personal notes in the notes field
- Favorite: Click the star button to favorite recipes
- Print: Click print button for print-friendly view
- Share: Click share button to generate shareable URL
Recipe History
- Click the 📚 button (top-right) to open recipe history
- Search recipes by name, ingredients, or content
- Filter by favorites
- Export all recipes as JSON or text
- Click any recipe to reload it
Dark Mode
- Click the 🌙 button (top-left) to toggle dark/light mode
- Preference is saved automatically
Monitoring
Health checks:
curl https://chomptron.com/health
curl https://chomptron.com/ready
curl https://chomptron.com/api/usage # View usage statistics
View logs:
# Recent logs
gcloud run logs read chomptron --region us-central1 --limit 50
# Live stream
gcloud run logs tail chomptron --region us-central1
Console dashboards:
Features
Core Functionality
- ✨ AI-powered recipe generation using Google Gemini
- 🍳 Creative recipe names and instructions
- 📏 Precise measurements and serving sizes
- ⏱️ Cooking time estimates (prep, cook, total time)
- 🎨 Modern Visual Polish: Glassmorphism design with staggered entrance animations
- 🔠 Premium Typography: Outfit and Inter fonts for a contemporary feel
- ⚡ Serverless, auto-scaling infrastructure on Google Cloud Run
Recipe Management
- 📚 Recipe history with localStorage persistence (up to 100 recipes)
- ⭐ Favorites system to mark and filter beloved recipes
- 🔍 Search and filter through saved recipes
- 💾 Export recipes to JSON or text format
- 📋 Quick access to past recipes via sidebar panel
- 📝 Add personal notes to each recipe
- ⭐ Rate recipes with 5-star rating system
Recipe Customization
- 🥗 Dietary Preferences & Allergies: Vegan, Vegetarian, Gluten-Free, Dairy-Free, Nut-Free, Shellfish-Free, Egg-Free, Soy-Free
- 📊 Recipe Scaling: Adjust serving sizes from 0.25x to 4x with automatic ingredient scaling
- 🖨️ Print-Friendly View: Clean print layout optimized for printing recipes
- 🔗 Shareable URLs: Generate shareable links for recipes (Web Share API support)
Recipe Display
- 📋 Structured Recipe Format: Parsed display with organized sections:
- Recipe name
- Serving size and timing information
- Ingredients list
- Step-by-step instructions
- Cooking tips
- 🎨 Dark/Light Mode: Toggle between themes with persistent preference
Performance & Optimization
- 💾 Recipe Caching: In-memory cache reduces API calls for similar ingredient combinations
- 🔄 Smart Retry Logic: Automatic retry with exponential backoff for quota errors
- ⚡ Rate Limiting: Client-side rate limiting prevents excessive API calls
Technical Features
- 🔍 Health monitoring and readiness checks
- 🔎 SEO optimized with meta tags, Open Graph, Twitter Cards, and structured data
- 📱 PWA support with manifest.json
- 🤖 robots.txt and sitemap.xml for search engine indexing
Recipe Data Structure
Recipes are stored in browser localStorage with the following structure:
{
"id": "timestamp",
"ingredients": "chicken, tomatoes, garlic",
"recipe": "Full recipe text...",
"recipeName": "Extracted recipe name",
"timestamp": "2025-01-01T00:00:00.000Z",
"favorite": false,
"rating": 0,
"notes": "",
"servingSize": 4,
"dietaryPreferences": {
"vegan": false,
"vegetarian": false,
"glutenFree": false,
"dairyFree": false,
"nutFree": false,
"shellfishFree": false,
"eggFree": false,
"soyFree": false
}
}
Caching Strategy
The application uses in-memory caching on the server side:
- Cache Key: Normalized ingredients + dietary preferences
- TTL: 24 hours
- Max Size: 100 recipes per instance
- Scope: Per-instance (serverless instances are ephemeral)
- Benefits: Reduces API calls for similar requests hitting the same instance
SEO Features
Optimized for search engines and social sharing with meta tags, Open Graph, Twitter Cards, structured data (JSON-LD), sitemap, robots.txt, and PWA support.
Buildkite Pipeline
This repo includes a Buildkite pipeline at .buildkite/pipeline.yml that runs on a self-hosted GCP agent provisioned by buildkite-gcp-agent.
Pipeline structure
Push / PR
└── Three steps dispatched in parallel:
├── :prettier: Format check (npm run format -- --check)
├── :eslint: Lint (npm run lint)
└── :nodejs: Tests (npm test)
└── Annotate — consolidated pass/fail surfaced in the Buildkite UI
The key difference from the GitHub Actions workflow, which runs format → lint → test sequentially in a single job: these three steps have no dependencies on each other and run simultaneously. On a pool of agents this distributes the work; even on a single agent the explicit dependency graph documents intent and makes the pipeline trivially scalable.
The deploy stage is intentionally omitted — Cloud Run deployments are handled by the existing GHA workflow which holds the production GCP credentials. CI (fast feedback) and CD (production access) are kept in separate systems as a security boundary.
Pipeline setup
Connect the repo via Buildkite → New Pipeline → point at github.com/swantron/chomptron. Buildkite reads .buildkite/pipeline.yml automatically on each push.
Agent targeting
All steps run on the gcp queue:
agents:
queue: gcp
Contributing
This is a personal project, but suggestions and improvements are welcome!
License
MIT
Advanced
- Delivery
- chomptron MCP server → your ahel gateway (mcp.ahel.ai) → every connected AI client.
- Catalog kind
- mcp-server
- Gateway key
io-github-swantron-chomptron- Source
- github.com/swantron/chomptron
- Hosted endpoint
https://chomptron.com/mcp