MXCP: Enterprise-Grade MCP Framework for AI Applications
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[](https://www.python.org/downloads/) [](https://github.com/raw-labs/mxcp/blob/main/LICENSE)
The structured methodology for building production-ready MCP servers with enterprise security, data quality, and comprehensive testing
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🚀 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
- 📊 Data Modeling First: Start with dbt models, data contracts, and quality tests
- 📋 Service Design: Define types, security policies, and API contracts upfront
- 🛠️ Smart Implementation: Choose SQL for data, Python for logic - or combine both
- ✅ Quality Assurance: Validate, test, lint, and evaluate before deployment
- 🚨 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
# One config enables enterprise features
auth: { provider: github }
audit: { enabled: true }
policies: { strict_mode: true }
telemetry: { enabled: true, endpoint: "http://otel-collector:4318" }
🎯 60-Second Quickstart
Experience