mxcp

**The structured methodology for building production-ready MCP servers with enterprise security, data quality, and comprehensive testing**
🚀 What Makes MXCP Different?
MXCP isn't just another MCP implementation - it's a **complete methodology** for building production AI applications the right way:
The Production-Ready Approach
1. **📊 Data Modeling First**: Start with dbt models, data contracts, and quality tests
2. **📋 Service Design**: Define types, security policies, and API contracts upfront
3. **🛠️ Smart Implementation**: Choose SQL for data, Python for logic - or combine both
4. **✅ Quality Assurance**: Validate, test, lint, and evaluate before deployment
5. **🚨 Production Operations**: Monitor drift, track audits, ensure performance
Enterprise Features Built-In
- 🔒 **Security First**: OAuth authentication, RBAC, policy enforcement
- 📝 **Complete Audit Trail**: Track every operation for compliance
- 🎯 **Type Safety**: Comprehensive validation across SQL and Python
- 🧪 **Testing Framework**: Unit tests, integration tests, LLM behavior tests
- 📈 **Performance**: Optimized queries, caching strategies, async support
- 🔄 **Drift Detection**: Monitor schema changes across environments
- 🔍 **OpenTelemetry**: Distributed tracing and metrics for production observability
🎯 60-Second Quickstart
Experience the power of MXCP in under a minute:
**Result**: You now have a production-ready AI tool API with type safety, validation, audit trails, and policy enforcement.
📚 The MXCP Methodology
Building production MCP servers requires more than just connecting data to AI. MXCP provides a structured approach:
1. Start with Data Quality
2. Design Your Service
3. Implement Smartly
- **SQL** for data queries against your dbt models
- **Python** for complex logic, ML models, and integrations
- **Both** working together for complete solutions
4. Test Everything
5. Deploy with Confidence
👉 **Read the full Production Methodology Guide** to learn how to build MCP servers the right way.
Choose Your Implementation Style
**SQL for Data Queries**
**Python for Complex Logic**
💡 Real-World Example: Combining SQL & Python
See how MXCP enables sophisticated workflows by combining the strengths of different tools:
**What just happened?**
1. **dbt models** fetch and transform COVID data from Our World in Data into DuckDB tables
2. **DuckDB** stores the transformed data locally for lightning-fast queries
3. **SQL endpoints** query the DuckDB tables for simple aggregations
4. **Python endpoints** can perform complex analysis on the same data
5. **Audit logs** track every query and function call for compliance
6. **Policies** enforce who sees what data across both SQL and Python
Ask Claude: *"Show me COVID vaccination rates in Germany vs France"* - SQL queries the data instantly
Ask Claude: *"Predict the trend for next month"* - Python runs ML models on the same data
This demonstrates MXCP's power: use the right tool for each job while maintaining consistent security and governance.
🛡️ Enterprise Features
MXCP provides comprehensive enterprise capabilities across security, quality, and operations:
Security & Governance
- **Authentication & Authorization** - OAuth 2.0, RBAC, session management
- **Policy Enforcement** - Fine-grained access control and data filtering
- **Audit Logging** - Complete compliance trail
Quality Assurance
- **Validation** - Schema and type verification
- **Testing** - Comprehensive endpoint testing
- **Linting** - Metadata optimization for LLMs
- **LLM Evaluation** - Test AI behavior and safety
Operations & Monitoring
- **Drift Detection** - Schema change monitoring