Your Champion AI Coder. Build elite frontends, architect robust backends, prevent hallucinations, slash costs by 75%, and transform ideas into production-ready code—for experts and vibe coders alike.
Resto is your Champion AI Coder—a comprehensive MCP server that transforms AI assistants into elite development partners. It doesn’t just reduce tokens; it elevates every aspect of AI-assisted coding:
1. 🏆 Elite Code Quality Build production-ready systems with world-class standards:
2. 💰 Massive Cost Reduction Cut AI token usage by 65-82%, dramatically lowering API costs:
3. 🛡️ Hallucination Prevention Structured validation workflows eliminate AI errors:
4. 🚀 Vibe Coder Empowerment Transform natural language into professional code:
Senior Engineers: Ship faster with elite-quality code at fraction of cost
Teams: Consistent standards, reduced review time, lower infrastructure bills
Beginners: Learn professional patterns while building real projects
Vibe Coders: Turn creative ideas into production code without deep technical knowledge
Startups: World-class quality without world-class budgets
Option 1: Install from GitHub (Recommended)
# Clone the repository
git clone https://github.com/IrshaGlobal/resto.git
cd resto
# Install dependencies
npm install
# Build the server
npm run build
Option 2: Quick Setup with npx
If you have Node.js installed, you can use it directly without cloning:
# Use npx to run directly from GitHub
npx github:IrshaGlobal/resto
Note: For production use, we recommend Option 1 (cloning) for better control and updates.
Choose your IDE and add the configuration:
💡 Tip: Replace
/absolute/path/to/with your actual installation path.Find your path:
- Windows:
cd resto && echo %CD%\dist\index.js- Mac/Linux:
cd resto && pwdthen append/dist/index.js- Example:
/home/user/resto/dist/index.jsorC:\Users\user\resto\dist\index.js
Resto provides two types of skills: Prompts (change AI behavior) and Tools (immediate actions).
Invoke these to transform AI responses for the rest of your conversation:
| Prompt | Description | Best For |
|---|---|---|
/resto |
Core compression mode (optional: intensity) |
General conversations |
/resto-help |
Display quick reference card | Learning commands |
/resto-review |
One-line code review comments | Code reviews, PRs |
/resto-commit |
Generate commit messages from diffs | Git workflow |
/resto-compress |
Compress text/files to resto style | Reducing verbosity |
/resto-docs |
Write terse documentation | API docs, READMEs |
/resto-debug |
Explain errors: WHAT → WHY → FIX | Troubleshooting |
/resto-stats |
Analyze token savings | Measuring efficiency |
/resto-backend |
Backend architecture & systems design | System planning |
/resto-frontend |
Elite frontend UI/UX design | Interface design |
/resto-chat |
Chat-only mode. NO code generation | Discussions, Q&A |
/resto-plan |
Structured planning. Prevent hallucinations before coding. | Complex task breakdown |
/resto-verify |
Validate code accuracy post-implementation. | Quality assurance |
/resto-learn |
Educational mode. Teach while building for beginners. | Learning & vibe coding |
Call these with data to get instant resto-formatted output:
| Tool | Input | Output | Use Case |
|---|---|---|---|
resto_review |
code, optional context |
One-line review comments | Code review automation |
resto_commit |
diff, optional type |
Resto-style commit message | Commit message generation |
resto_compress |
text, optional intensity |
Compressed text | Text compression |
resto_docs |
topic, optional format |
Terse documentation | Documentation writing |
resto_debug |
error, optional context |
Minimal error analysis | Error explanation |
resto_stats |
text, optional intensity |
Token savings report | Efficiency metrics |
resto_backend |
requirements, optional stack |
Backend architecture design | System architecture |
resto_frontend |
product, optional style |
Frontend UI/UX design system | UI/UX planning |
Choose the right compression level for your needs:
| Level | Token Savings | Style | Example |
|---|---|---|---|
| Lite | ~35% | Slightly terse, remove obvious fluff | “Function returns error on null param” |
| Full | ~72% | Full minimal mode (default) | “Function returns error. Parameter null.” |
| Ultra | ~79% | Telegraphic, abbreviate common words | “Fn err. Param null.” |
| Zen | ~82% | Poetic minimal, one insight per line | “Error. Null param. Check first.” |
Usage:
/resto # Default: full intensity
/resto lite # Light compression
/resto ultra # Maximum brevity
/resto zen # Poetic minimalism
Resto implements multi-layered safeguards against AI hallucinations:
/resto-plan): Decompose tasks, verify feasibility, identify risks/resto-chat): Discuss architecture before writing code/resto-verify): Comprehensive accuracy checklist/resto-review): Catch hallucinated APIs and undefined variablesConfident, accurate code you can trust—no more silent hallucinations causing bugs in production.
Input:
const user = await db.users.findById(id);
console.log(user.name);
Resto Review Output:
[BLOCK] line 2 - no null check on user -> add guard: if (!user) throw new Error('User not found')
[NIT] line 1 - missing type annotation -> const user: User | null
Input Diff:
+ export function validateEmail(email: string): boolean {
+ return /^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email);
+ }
Resto Commit Output:
feat: add email validation helper
Input Error:
TypeError: Cannot read properties of undefined (reading 'map')
at App.tsx:45:12
Resto Debug Output:
WHAT: map called on undefined
WHY: Variable `items` not initialized or API returned null
FIX: Add guard: const items = data?.items ?? []
Topic: Email validation function
Resto Docs Output:
## validateEmail(email: string): boolean
Validates email format using RFC 5322 regex.
Returns true if valid, false otherwise.
Example:
validateEmail("user@example.com") // true
validateEmail("invalid") // false
See how Resto transforms natural language into professional code:
Vibe Prompt:
“Make a clean, modern landing page for my SaaS product”
Resto Translation:
Minimalist design. Generous whitespace. Sans-serif typography (Inter). Subtle shadows (elevation-1). 8px spacing scale. Primary CTA above fold. Social proof section. Feature grid (bento layout). Footer with links. Responsive mobile-first.
Implementation: React + Next.js + Tailwind + ShadCN components with accessibility (WCAG AA), semantic HTML, optimized images, smooth scroll animations.
Vibe Prompt:
“I need users to sign up and login securely”
Resto Planning:
TASK: Secure authentication system
STEPS:
1. Choose auth provider (Clerk recommended for speed, Auth0 for enterprise)
2. Setup protected routes middleware
3. Implement signup form with validation (Zod schema)
4. Implement login form with error handling
5. Add password reset flow
6. Session management (JWT + refresh tokens)
RISKS:
- Rate limiting needed to prevent brute force -> Add express-rate-limit
- Password storage -> Use provider, never store plaintext
QUESTIONS:
- Social login needed (Google, GitHub)?
- Email verification required?
Result: Production-ready auth with security best practices, explained step-by-step for learning.
Token savings measured across different content types:
| Content Type | Lite | Full | Ultra | Zen |
|---|---|---|---|---|
| Technical Docs | 35% | 72% | 79% | 82% |
| Code Reviews | 30% | 65% | 70% | 75% |
| Commit Messages | 25% | 50% | 60% | 65% |
| Error Explanations | 40% | 70% | 75% | 80% |
| API Documentation | 35% | 70% | 75% | 78% |
Results vary by content complexity. Average savings: 65-75%.
/resto ultra # Set maximum brevity
Describe feature requirements # AI asks clarifying questions
/resto-backend # Architecture design with tradeoffs
Let's code # Exit chat, start implementation
/resto-verify # Validate generated code
git commit # /resto-commit for message
/resto-learn # Enable educational mode
"I want to build a todo app" # Natural language description
AI: Asks questions, explains concepts
/resto-plan # Break into manageable steps
Approve plan # Review and confirm
Let's code # Implementation with explanations
Ask questions # "Why did you use useState here?"
Iterate and learn # Build confidence progressively
/resto full # Standard team intensity
Architecture discussion # /resto-chat for planning
/resto-backend # Shared architectural standards
Code review # /resto-review for PRs
Documentation # /resto-docs for API specs
Commit # /resto-commit for consistent messages
Savings: Teams average 70% token reduction = $500-2000/month saved on API costs (depending on usage).
┌─────────────┐ ┌──────────────┐ ┌─────────────┐
│ Your IDE │────▶│ Resto MCP │────▶│ AI Model │
│ (Client) │◀────│ Server │◀────│ (Provider) │
└─────────────┘ └──────────────┘ └─────────────┘
│
┌─────┴─────┐
│ Skills │
│ Database │
└───────────┘
1. Prompts (Behavior Change)
2. Tools (Immediate Actions)
Yes! Resto works with any MCP-compatible AI provider. The quality depends on how well the model follows system instructions.
Currently, Resto uses predefined compression rules. Custom rule sets may be added in future versions.
Problem: Error: Cannot find module '@modelcontextprotocol/sdk'
Solution:
npm install
npm run build
Problem: Server configured but prompts/tools don’t appear.
Solution:
dist/index.jsnode dist/index.js manually to check for errorsnode --version)Problem: Invoking /resto produces no output.
Solution:
Problem: Calling resto_review returns template, not actual review.
Solution: This is expected. Tools return formatted prompts for AI processing. For immediate output, use prompts like /resto-review instead.
Problem: Not seeing 70%+ reduction.
Solution:
/resto ultra or /resto zen# Watch mode (during development)
npm test
# Single run (for CI)
npm run test:run
Test Coverage:
resto-mcp-server/
├── src/
│ ├── index.ts # MCP server implementation
│ └── skills.ts # Skill definitions
├── tests/
│ └── server.test.ts # Test suite (13 tests)
├── dist/ # Compiled output
├── package.json
├── tsconfig.json
└── README.md
Contributions welcome! Please:
git checkout -b feature/amazing-feature)git commit -m 'Add amazing feature')git push origin feature/amazing-feature)MIT License — See LICENSE for details.