feat: MCP Server Phase 4 - Production Ready Features (CFOS-44)#49
Merged
jayscambler merged 2 commits intomainfrom Jun 23, 2025
Merged
feat: MCP Server Phase 4 - Production Ready Features (CFOS-44)#49jayscambler merged 2 commits intomainfrom
jayscambler merged 2 commits intomainfrom
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…S-44) Comprehensive monitoring system for production-ready MCP deployments: ## Monitoring Components: ### 1. MetricsCollector - Central metrics aggregation with in-memory buffers - Periodic flushing to Lance dataset - Configurable retention and aggregation intervals ### 2. UsageTracker - Document access pattern tracking - Query performance analytics - Agent activity monitoring - Top documents and queries identification ### 3. PerformanceMonitor - Operation performance tracking with percentiles - Real-time performance snapshots - Response time distribution analysis - Error rate and success rate monitoring ### 4. CostCalculator - LLM API cost tracking (OpenAI, Anthropic, Cohere) - Storage operation cost attribution - Bandwidth usage monitoring - Cost reports with daily breakdowns ### 5. MCP Monitoring Tools (5 new tools) - get_usage_metrics: Access patterns and query statistics - get_performance_metrics: Operation performance and trends - get_cost_report: Cost attribution and projections - get_monitoring_status: System health and buffer status - export_metrics: Prometheus/JSON export capabilities ## Integration Features: - MonitoredMessageHandler: Automatic operation tracking - MonitoredToolRegistry: LLM cost tracking for enhancement tools - Zero-overhead when monitoring is disabled - Transport-agnostic design (works with stdio and HTTP) ## Configuration: Added monitoring settings to MCPConfig: - monitoring_enabled (default: True) - monitoring_retention_days (default: 30) - monitoring_flush_interval (default: 60s) - pricing_config_path (optional custom pricing) ## Testing: Comprehensive test suite with 20+ test cases covering: - Metrics collection and buffering - Usage tracking and aggregation - Performance monitoring with percentiles - Cost calculation and reporting - Tool integration - Message handler monitoring This provides production-grade observability for MCP deployments\!
- Authentication system with multiple providers: - API key authentication with secure hashing - OAuth 2.1 with PKCE support - JWT token handling with RS256/HS256 support - Multi-auth provider for chaining auth methods - Authorization with role-based access control: - Standard roles (viewer, editor, admin, monitor, service) - Permission-based authorization - Resource-level policies with conditions - Wildcard permission support - Rate limiting system: - Global, per-client, and per-operation limits - Token bucket and sliding window algorithms - Configurable limits and burst sizes - Rate limit status reporting - Audit logging for security events: - Comprehensive event types (auth, authz, rate limit, etc.) - Multiple storage backends (memory, file, dataset) - Event search and filtering - Sensitive data redaction - Configurable retention policies - Security middleware integration: - SecuredMessageHandler for all MCP operations - Automatic security checks on every request - Integration with monitoring system - Configurable anonymous access - Server configuration: - MCPConfig extended with security settings - Support for multiple auth providers - Configurable audit retention - Optional anonymous permissions - Comprehensive test suite with 88% coverage
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Summary
This PR implements Phase 4 of the MCP server - Production Ready features, including comprehensive monitoring and security systems.
Phase 4.1: Monitoring System
Components Implemented:
MCP Tools Added:
get_usage_metrics: Usage statistics with time-based aggregationget_performance_metrics: Performance metrics with percentilesget_cost_report: Cost attribution by agent/operation/providerget_monitoring_status: System health and buffer statusexport_metrics: Export to Prometheus/JSON formatsIntegration:
Phase 4.2: Security System
Authentication:
Authorization:
Rate Limiting:
Audit Logging:
Configuration
New MCPConfig options:
Testing
Performance Impact
Related Issues
Next Steps