MCP-Booster
About
An MCP server with CoConuT (Continuous Chain of Thought) for use with the Cursor IDE, distributed as a global NPM package.
Details
- Author
- llm-booster
- Categories
- Developer Tools, AI, Other
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"Automatic planning with Booster_Steps transformed our execution capacity. Each task comes with context, commands, and ready-made checks."
- Pedro Costa, Startup CTO
Environment Variables for Maximum Performance
# Linux/macOS - Power Users export MCP_BOOSTER_API_KEY="your_api_key_here" export MCP_BOOSTER_LOG_LEVEL="info" # Windows - Professionals set MCP_BOOSTER_API_KEY=your_api_key_here set MCP_BOOSTER_LOG_LEVEL=info
mcp-booster --api-key YOUR_KEY # Define API key mcp-booster --help # Show help mcp-booster --version # Show version
# Be part of the change git clone https://github.com/llm-booster/mcp-booster.git cd mcp-booster # Set up your innovation environment npm install && npm run build # Test your ideas npm run dev && npm test
LLM Booster was developed following General Data Protection Regulation principles:
- 🛡️ Fully GDPR Compliant: Transparency and security first
- 🏠 Local Processing: Whenever possible, minimizing data transfer
- 🔐 Total Control: Access, correct, or request deletion of your data at any time
- 📊 Absolute Transparency: You always know how your data is used
Based on results from 34,000+ active users:
- 🛡️Bugs Prevented: Average of 127 bugs prevented per dev
- ⚡Time Saved: Up to 40h/week saved in debugging
- 💪Confidence Boost: +300% technical confidence
- 🎯Deploy Success: 100% deployment success rate
- 🔧Zero Rollbacks: 95% reduction in rollbacks
- 🏆Code Master: Recognition as technical reference
# Always at the forefront npm update -g mcp-booster # Discover new features npm outdated -g mcp-booster
- ✅34,000+ developersalready saved from bugs
- ⭐4.9/5 starssatisfaction rating
- ⚡90% fewer bugsin production
- 🚀5x more productivethan traditional solutions
- 😴0 nights lostdebugging until 3 AM
We follow an internal decision tree that guarantees logical, safe, and verifiable responses at every step. It's not luck, it's science applied to development.
- 🧠Validation Algorithm: Eliminates rushed responses and common AI model errors
- 🎯Auditable System: Reusable functions that make the process scalable and evolutionary
- 🛡️GDPR Security: Local processing whenever possible, your data protected
- 💡Eliminates Anxiety: Stop debugging until 3 AM - become the confident ninja you always wanted to be
- 📈Proven ROI: -6h debugging/week, +200% technical confidence, 97% would use again
Perfect integration with the tools you already use and love:
- Cursor✅ (Optimized)
- Cline✅
- Windsurf✅
- GitHub Copilot✅
- 🧠 Think structured, not random- 90% fewer "Oops, forgot that part"
- 🎯 Validation that seniors would approve- PRs approved on first try
- 🚀 Deploy Friday without fear- Zero bugs in production
- 📚 Become the team's technical reference- Recognition as organized dev
- 🆘GitHub Issues:Immediate Technical Support
- 💡Documentation:Complete Wiki
- 🗣️Discussions:Community Forum
- 🌐Official Site:llmbooster.com
- 📧Direct Email:[help@llmbooster.com
- ✅24h responsefor critical issues
- 🔧Personalized installationsupport
- 📚Free trainingfor enterprise teams
- 🚀Assisted migrationfrom other AI assistants
MCP-Booster was created specifically to solve these pain points:
- 🧠 Truly Intelligent Reasoning: CoConuT (Continuous Chain of Thought) system that thinks like you, but without limitations
- 🎯 Deep Understanding: Maintains complete project context and decision history
- 🚀 Automated Execution: Doesn't just suggest, butexecutescomplete solutions
- 📊 Self-Validation: Checks its own reasoning quality before acting
- 🔧 Intelligent Planning: Breaks down complex tasks into executable steps
- 💾 Persistent Memory: Remembers all modifications and decisions made
Unlike other assistants that give "shallow" responses, MCP-Boosterthinks in chains, exploring multiple solutions, validating approaches, and arriving at the best possible answer.
Developed by](https://llmbooster.com)[LLM Booster, the leading company in AI productivity solutions, combining years of Large Language Model research with real software development experience.
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