MCP Kafka Schema Reg

# Kafka Schema Registry MCP Server - Use Cases This document outlines various real-world use cases and scenarios for the Kafka Schema Registry MCP Server v2.0.0 with **FastMCP 2.8.0+ Framework**, **MCP 2025-06-18 Specification Compliance**, **Enhanced Authentication**, **Claude Desktop Integration**, **Async Task Management**, and **Multi-Registry Support**. ## 🤖 MCP Integration Overview The Kafka Schema Registry MCP Server uses the **modern FastMCP 2.8.0+ framework** with full **MCP 2025-06-18 specification compliance**, integrating seamlessly with Claude Desktop and other MCP clients. Users interact with schema management through **natural language commands** with enhanced performance, reliability, and authentication capabilities. ### **Key MCP Features:** - ✅ **FastMCP 2.8.0+ Framework**: Modern MCP architecture with enhanced performance - ✅ **MCP 2025-06-18 Compliance**: Full support for latest Message Control Protocol specification - ✅ **Enhanced Authentication**: Native FastMCP BearerAuth with OAuth 2.0 support for 5 providers - ✅ **48 MCP Tools**: Complete schema operations via natural language - ✅ **Claude Desktop Ready**: Direct AI integration for schema management - ✅ **Natural Language Interface**: No curl commands or API knowledge required - ✅ **Real-time Interaction**: Immediate feedback through Claude Desktop - ✅ **Context-Aware Operations**: All tools support schema contexts - ✅ **Export Capabilities**: JSON and Avro IDL formats with comprehensive metadata - ✅ **Async Task Management**: Non-blocking operations with progress tracking - ✅ **Multi-Registry Support**: Manage multiple Schema Registry instances --- ## 🏢 Enterprise Use Cases ### 1. Multi-Environment Schema Management with AI Assistant #### **Scenario**: Large Enterprise with Multiple Deployment Environments A technology company with microservices architecture needs to manage schemas across development, staging, and production environments using AI-assisted workflows. **Claude Desktop Implementation:** ``` **Schema Development Workflow:** ``` Human: "Register a user event schema in the development context with fields for userId, eventType, and timestamp" Claude: I'll register that user event schema in your development context. [Uses register_schema MCP tool] ✅ Schema registered: user-events (ID: 1) in development context ``` **Promotion Workflow:** ``` Human: "Check if the user-events schema from development is compatible with production, then promote it to staging if compatible" Claude: I'll check compatibility first and then promote if safe. [Uses check_compatibility MCP tool] ✅ Compatibility check: Schema is BACKWARD compatible with production [Uses register_schema MCP tool] ✅ Schema promoted to staging context: user-events (ID: 2) ``` **MCP Benefits:** - ✅ **Natural Language**: No technical API knowledge required - ✅ **AI-Assisted**: Claude helps with complex workflows - ✅ **Environment Isolation**: Prevents accidental production impact - ✅ **Instant Feedback**: Real-time validation and results --- ### 2. Multi-Tenant SaaS Platform with Context Isolation #### **Scenario**: SaaS Platform with Client-Specific Schema Requirements A platform serving multiple clients where each client may have customized data structures and compliance requirements. **Client Setup:** ``` Human: "Create separate contexts for our clients: ACME Corp, Globex Industries, and Initech" Claude: I'll set up isolated contexts for each of your clients. [Uses create_context MCP tool for each client] ✅ Created context: client-acme ✅ Created context: client-globex ✅ Created context: client-initech ``` **GDPR-Compliant Schema for EU Client:** ``` Human: "Register a user profile schema in the ACME context with GDPR compliance fields including consent and retention period" Claude: I'll create a GDPR-compliant user profile schema for ACME. [Uses register_schema MCP tool] ✅ Registered schema: user-profile in client-acme context Fields: userId, email, gdprConsent, dataRetentionDays (default: 365) ``` **HIPAA-Compliant Schema for Healthcare Client:** ``` Human: "Register a similar user profile schema for Globex but with HIPAA compliance fields instead of GDPR" Claude: I'll create a HIPAA-compliant version for Globex Industries. [Uses register_schema MCP tool] ✅ Registered schema: user-profile in client-globex context Fields: userId, email, hipaaCompliant, encryptionLevel (default: AES256) ``` **MCP Benefits:** - ✅ **Complete Tenant Isolation**: Each client has their own schema space - ✅ **Compliance-Specific Schemas**: GDPR, HIPAA, PCI variations - ✅ **Natural Language Specification**: Describe compliance requirements in plain English - ✅ **Independent Evolution**: Clients can evolve schemas independently --- ## 🔧 Development Workflow Use Cases ### 3. Schema Evolution with AI-Assisted Compatibility Testing #### **Scenario**: Safe Schema Evolution with Backward Compatibility A development team needs to evolve their order schema while ensuring all dependent services remain functional. **Current State Analysis:** ``` Human: "Show me the current order schema in production and its version history" Claude: Let me check the current state of your order schema. [Uses get_schema and get_schema_versions MCP tools] 📋 Current schema: order-events v3 in production Fields: orderId, customerId, amount, currency 📊 Version history: v1, v2, v3 (3 versions total) ``` **Evolution Design:** ``` Human: "I want to add optional fields for shipping address and order notes. Check if this evolution is backward compatible" Claude: I'll design the evolution and test compatibility for you. [Uses check_compatibility MCP tool] ✅ Evolution design: Adding optional fields with defaults ✅ Compatibility test: BACKWARD compatible - safe to deploy New fields: shippingAddress (optional), orderNotes (optional, default: null) ``` **Staged Rollout:** ``` Human: "Register the evolved schema in development first, then promote to staging if tests pass" Claude: I'll implement a staged rollout for safety. [Uses register_schema MCP tool] ✅ Registered evolved schema: order-events v4 in development 🧪 Ready for testing in development environment ``` **MCP Benefits:** - ✅ **AI-Assisted Evolution**: Claude helps design backward-compatible changes - ✅ **Automated Compatibility Testing**: Instant validation before deployment - ✅ **Staged Rollout Support**: Safe promotion through environments - ✅ **Risk-Free Iteration**: Test in development without production impact --- ### 4. Feature Branch Schema Development #### **Scenario**: Isolated Schema Development for New Features Developers working on a new user preferences feature need isolated schema development without affecting the main development branch. **Feature Branch Setup:** ``` Human: "Create a feature context for the new user preferences feature we're developing" Claude: I'll create an isolated context for your user preferences feature. [Uses create_context MCP tool] ✅ Created context: feature-user-preferences ``` **Feature Schema Development:** ``` Human: "Register a schema for user preference updates with userId, preferences map, and timestamp" Claude: I'll create the user preferences schema in your feature context. [Uses register_schema MCP tool] ✅ Registered schema: user-preferences-updated in feature-user-preferences context Fields: userId (string), preferences (map<string>), updatedAt (long) ``` **Integration Testing:** ``` Human: "Show me all schemas in the feature context and check if they're compatible with main development" Claude: I'll review your feature schemas and test compatibility. [Uses list_subjects and check_compatibility MCP tools] 📋 Feature schemas: user-preferences-updated ✅ Compatibility with development: FORWARD compatible - safe to merge ``` **Cleanup After Merge:** ``` Human: "The feature has been merged. Clean up the feature context" Claude: I'll clean up the feature context for you. [Uses delete_context MCP tool] ✅ Deleted context: feature-user-preferences ♻️ Resources cleaned up ``` **MCP Benefits:** - ✅ **Isolated Development**: No conflicts with main development - ✅ **Easy Cleanup**: Simple context deletion after merge - ✅ **Parallel Feature Work**: Multiple teams can work independently - ✅ **Integration Validation**: Test compatibility before merging --- ## 📊 Export & Documentation Use Cases ### 5. Comprehensive Schema Documentation Generation #### **Scenario**: Documentation Team Needs Human-Readable Schema Documentation A documentation team needs to generate comprehensive, human-readable documentation for all schemas across different environments for developer onboarding and API documentation. **Documentation Export:** ``` Human: "Export all production schemas in Avro IDL format for our API documentation" Claude: I'll export your production schemas in human-readable Avro IDL format. [Uses export_context MCP tool] ✅ Exported production context: 15 schemas in Avro IDL format 📋 Schemas include: user-events, order-events, payment-events, etc. 🔗 Generated beautiful documentation with field descriptions ``` **Example Generated Documentation:** ```idl @namespace("com.company.events") /** User event tracking schema */ record UserEvent { /** Unique user identifier */ string userId; /** Type of event performed */ string eventType; /** Event timestamp in milliseconds */ long timestamp; /** Optional event metadata */ map<string> metadata = {}; } ``` **Version History Export:** ``` Human: "Export the complete version history for the user-events schema including all metadata" Claude: I'll export the complete history of your user-events schema. [Uses export_subject MCP tool] ✅ Exported user-events: All 4 versions with complete metadata 📊 Version progression: v1 → v2 (added email) → v3 (added metadata) → v4 (current) 📋 Includes: compatibility levels, registration timestamps, evolution notes ``` **MCP Benefits:** - ✅ **Human-Readable Output**: Beautiful Avro IDL with documentation - ✅ **Complete History**: All versions and evolution metadata - ✅ **AI-Enhanced Documentation**: Claude adds context and explanations - ✅ **Multiple Formats**: JSON for tooling, IDL for humans --- ### 6. Compliance and Audit Export #### **Scenario**: Financial Institution Needs Regulatory Compliance Exports A financial institution must generate regular exports for regulatory compliance, including GDPR data lineage and SOX audit requirements. **GDPR Compliance Export:** ``` Human: "Export all schemas from our GDPR context with complete metadata for our quarterly compliance report" Claude: I'll generate a comprehensive GDPR compliance export for you. [Uses export_context MCP tool] ✅ Exported GDPR context: 8 schemas with full compliance metadata 📋 Included: data retention policies, consent tracking, subject rights 📅 Export timestamp: Ready for Q4 2024 compliance submission ``` **SOX Audit Trail:** ``` Human: "Generate an audit trail showing all schema changes in the financial context for the past quarter" Claude: I'll create a complete audit trail for your SOX compliance. [Uses export_context with version history] ✅ Generated audit trail: financial context 📊 Schema changes: 12 modifications across 6 schemas 🔍 Includes: who, what, when, compatibility impacts 📋 SOX-compliant format with digital signatures ``` **PCI DSS Security Review:** ``` Human: "Show me all payment-related schemas and verify they don't contain sensitive cardholder data" Claude: I'll review your payment schemas for PCI compliance. [Uses list_subjects and get_schema MCP tools] ✅ Reviewed payment schemas: 3 schemas analyzed 🔒 PCI Compliance: All schemas use tokenized card references only ⚠️ No sensitive cardholder data found in schema definitions 📋 Ready for PCI assessment ``` **MCP Benefits:** - ✅ **Regulatory-Ready Exports**: Formatted for compliance submissions - ✅ **Complete Audit Trails**: Who changed what and when - ✅ **Security Validation**: AI-assisted compliance checking - ✅ **Automated Reporting**: Scheduled compliance exports --- ## 🎛️ Advanced Configuration Use Cases ### 7. Dynamic Configuration Management #### **Scenario**: Operations Team Needs to Manage Compatibility and Mode Settings A platform operations team needs to dynamically adjust schema compatibility requirements and operational modes across different environments. **Environment-Specific Configuration:** ``` Human: "Set the production context to FULL compatibility mode for maximum safety, but keep development at BACKWARD for flexibility" Claude: I'll configure different compatibility levels for your environments. [Uses update_global_config MCP tool] ✅ Production context: Set to FULL compatibility (maximum safety) ✅ Development context: Set to BACKWARD compatibility (flexibility) 📋 Configuration applied to all subjects in respective contexts ``` **Mode Management:** ``` Human: "Switch the schema registry to VIEWONLY mode during our maintenance window" Claude: I'll switch to read-only mode for your maintenance. [Uses update_mode MCP tool] ✅ Global mode: Changed to VIEWONLY 🚫 Schema registration temporarily disabled ⏰ Ready for maintenance operations ``` **Subject-Specific Overrides:** ``` Human: "Set stricter FULL compatibility for our critical payment schemas but keep BACKWARD for internal events" Claude: I'll apply subject-specific compatibility settings. [Uses update_subject_config MCP tool] ✅ payment-events: Set to FULL compatibility ✅ payment-processed: Set to FULL compatibility ✅ internal-events: Kept at BACKWARD compatibility 🔒 Critical payments now have maximum safety ``` **MCP Benefits:** - ✅ **Dynamic Configuration**: Real-time adjustments without restarts - ✅ **Context-Aware Settings**: Different rules per environment - ✅ **Subject-Level Control**: Fine-grained compatibility management - ✅ **Natural Language Commands**: No complex configuration syntax --- ## 🚀 Real-World Workflow Examples ### 8. Complete Development Lifecycle #### **Scenario**: End-to-End Schema Development Workflow A complete example showing how a team develops, tests, and deploys a new schema using the MCP server. **Day 1: Initial Development** ``` Human: "We're starting a new feature for order tracking. Create a feature context and register an initial schema with orderId, status, and timestamp" Claude: I'll set up your order tracking feature development. [Uses create_context and register_schema MCP tools] ✅ Created context: feature-order-tracking ✅ Registered schema: order-status-updated v1 Fields: orderId (string), status (enum), timestamp (long) ``` **Day 3: Schema Evolution** ``` Human: "We need to add customer notification preferences and estimated delivery date to the order status schema. Check compatibility first" Claude: I'll evolve your schema and validate compatibility. [Uses check_compatibility and register_schema MCP tools] ✅ Compatibility check: BACKWARD compatible evolution ✅ Registered schema: order-status-updated v2 Added: notificationPrefs (optional), estimatedDelivery (optional) ``` **Day 5: Integration Testing** ``` Human: "Move the order tracking schema to our integration testing context and run compatibility tests against all related schemas" Claude: I'll set up integration testing for your schema. [Uses register_schema and check_compatibility MCP tools] ✅ Schema registered in integration context ✅ Compatibility with: order-events (✓), customer-notifications (✓), delivery-events (✓) 🧪 Ready for integration testing ``` **Day 8: Production Deployment** ``` Human: "All tests passed! Promote the order tracking schema to staging, then production after staging validation" Claude: I'll execute the production deployment workflow. [Uses register_schema MCP tool for staged deployment] ✅ Promoted to staging: order-status-updated v2 ⏳ Waiting for staging validation... ✅ Promoted to production: order-status-updated v2 🎉 Feature successfully deployed! ``` **Cleanup:** ``` Human: "Clean up the feature and integration contexts since we're done" Claude: I'll clean up the temporary development contexts. [Uses delete_context MCP tool] ✅ Deleted context: feature-order-tracking ✅ Deleted context: integration-testing ♻️ Development resources cleaned up ``` **MCP Benefits:** - ✅ **Complete Workflow Support**: From development to production - ✅ **AI-Guided Process**: Claude helps with best practices - ✅ **Safety Validation**: Compatibility checks at every step - ✅ **Automated Cleanup**: No orphaned development resources --- ## 🚀 Async Operations Use Cases ### 9. Large-Scale Schema Migration with Progress Tracking #### **Scenario**: Migrating Hundreds of Schemas Between Environments A company needs to migrate their entire schema registry from staging to production, containing over 200 schemas across multiple contexts. **Initiating Large Migration:** ``` Human: "Migrate all schemas from the staging registry to production" Claude: I'll generate the Docker migration command for staging to production. [Uses migrate_context MCP tool] 🚀 Ready to migrate staging registry to production: Copy and run this command: docker run --platform linux/amd64 --network host -it --rm \ -e SOURCE_SCHEMA_REGISTRY_URL=http://staging-registry:8081 \ -e DEST_SCHEMA_REGISTRY_URL=http://prod-registry:8082 \ -e SOURCE_USERNAME=staging_user \ -e SOURCE_PASSWORD=staging_pass \ -e DEST_USERNAME=prod_user \ -e DEST_PASSWORD=prod_pass \ -e ENABLE_MIGRATION=true \ -e DRY_RUN=true \ -e PRESERVE_IDS=true \ -e DEST_IMPORT_MODE=true \ aywengo/kafka-schema-reg-migrator:latest Migration scope: All contexts from staging registry (215 schemas) Set DRY_RUN=false when ready for actual migration. ⚠️ This is a DRY RUN - no actual changes will be made ⚠️ Ensure Docker is installed and running ⚠️ Large migration - monitor output for progress ``` **Monitoring Migration Progress:** ``` Human: "How can I monitor the migration once it's running?" Claude: The external migrator provides comprehensive real-time output: # The Docker command shows live progress: - Current context being migrated - Schemas processed: 87/215 - Failed schemas with retry count - Estimated time remaining # Example output: 2024-06-07 16:49:15 - INFO - Processing subject: user-events 2024-06-07 16:49:15 - INFO - [DRY RUN] Would migrate user-events version 1 - compatible 2024-06-07 16:49:15 - INFO - Progress: 87/215 schemas (40% complete) For individual schema migrations, use migrate_schema which returns a task ID for monitoring with get_migration_progress. ``` **MCP Benefits:** - ✅ **Non-Blocking Operations**: Start migrations and continue working - ✅ **Real-Time Progress**: Monitor large operations without timeout - ✅ **Automatic Retry Logic**: Handles transient failures - ✅ **Detailed Progress Tracking**: Know exactly what's happening --- ### 10. Multi-Registry Schema Synchronization #### **Scenario**: Global Company with Regional Schema Registries A multinational company operates Schema Registries in different regions (US, EU, APAC) and needs to synchronize schemas while respecting data residency requirements. **Registry Health Check:** ``` Human: "Check the status of all our regional schema registries" Claude: I'll check the health of all your regional registries. [Uses test_all_registries MCP tool] 🏥 Registry Health Check: ✅ us-east: Connected (45ms) - Primary US registry ✅ eu-central: Connected (120ms) - EU GDPR-compliant registry ✅ apac-tokyo: Connected (180ms) - APAC regional registry ⚠️ us-west: Degraded (850ms) - High latency detected Overall: 3/4 healthy, 1 degraded ``` **Cross-Registry Comparison:** ``` Human: "Compare the customer schemas between US and EU registries to ensure GDPR compliance" Claude: I'll start comparing customer schemas across regions for compliance. [Uses compare_contexts_across_registries MCP tool] 🔄 Comparison started: Task ID: comp-20240531-us-eu-customer Comparing: US/customer ↔ EU/customer Focus: GDPR compliance fields Status: Running (15% - Analyzing field differences...) ``` **Selective Schema Promotion:** ``` Human: "Promote only the GDPR-compliant schemas from US to EU registry" Claude: I'll identify and migrate only GDPR-compliant schemas. [Uses migrate_schema MCP tool with filtering] 🔄 Selective migration started: Task ID: mig-20240531-gdpr-selective Source: us-east/customer Target: eu-central/customer Filter: GDPR-compliant schemas only Identified schemas: ✅ customer-profile-v3 (has consent fields) ✅ customer-preferences-v2 (has retention policy) ❌ customer-analytics-v1 (missing GDPR fields) Migrating: 2/3 schemas ``` **MCP Benefits:** - ✅ **Multi-Region Support**: Manage global infrastructure - ✅ **Compliance-Aware Migration**: Filter based on requirements - ✅ **Cross-Registry Comparison**: Identify differences - ✅ **Selective Synchronization**: Migrate only what's needed --- ### 11. Batch Environment Cleanup #### **Scenario**: Cleaning Up After Feature Development Sprint After a two-week sprint, the team has created multiple feature branch contexts that need to be cleaned up efficiently. **Identify Cleanup Candidates:** ``` Human: "Show me all contexts that start with 'feature-' so we can clean them up" Claude: I'll identify all feature branch contexts for cleanup. [Uses list_contexts MCP tool] 📋 Feature contexts found: • feature-user-dashboard (12 schemas) • feature-payment-v2 (8 schemas) • feature-mobile-api (15 schemas) • feature-analytics-beta (6 schemas) • feature-notifications (4 schemas) Total: 5 contexts with 45 schemas ``` **Batch Cleanup Preview:** ``` Human: "Clean up all those feature contexts, but show me what will be deleted first" Claude: I'll preview the batch cleanup operation for you. [Uses clear_multiple_contexts_batch MCP tool with dry_run=true] 🔍 Batch Cleanup Preview: Task ID: clean-20240531-features (DRY RUN) Will delete: 📁 feature-user-dashboard: • user-dashboard-events • dashboard-preferences • dashboard-widgets ... and 9 more schemas 📁 feature-payment-v2: • payment-flow-v2 • payment-confirmation-v2 ... and 6 more schemas Total: 5 contexts, 45 schemas Estimated time: ~15 seconds No changes made (dry run mode) ``` **Execute Parallel Cleanup:** ``` Human: "Looks good, go ahead and clean them all up" Claude: I'll execute the batch cleanup of all feature contexts. [Uses clear_multiple_contexts_batch MCP tool with dry_run=false] 🧹 Batch cleanup started: Task ID: clean-20240531-features Mode: Parallel deletion (10 concurrent operations) Status: Running Progress: ✅ feature-notifications: Completed (4/4 schemas deleted) ✅ feature-payment-v2: Completed (8/8 schemas deleted) 🔄 feature-user-dashboard: In progress (7/12 schemas deleted) ⏳ feature-mobile-api: Pending ⏳ feature-analytics-beta: Pending ``` **Monitor Cleanup Progress:** ``` Human: "How's the cleanup going?" Claude: Let me check the cleanup progress. [Uses get_cleanup_progress MCP tool] 📊 Cleanup Progress: clean-20240531-features Overall: 78% complete Duration: 8 seconds elapsed Completed: 3/5 contexts Schemas deleted: 35/45 Current: Deleting feature-mobile-api schemas Rate: 4.4 schemas/second Estimated completion: ~3 seconds ``` **MCP Benefits:** - ✅ **Parallel Execution**: Fast cleanup with concurrent operations - ✅ **Safe Preview Mode**: Review before deleting - ✅ **Progress Tracking**: Monitor large cleanup operations - ✅ **Efficient Resource Management**: Clean up test data quickly --- ### 12. Disaster Recovery with Progress Monitoring #### **Scenario**: Emergency Schema Registry Recovery A production incident requires restoring schemas from a backup registry to a new instance. **Initiate Emergency Recovery:** ``` Human: "We need to restore all production schemas from our backup registry to the new disaster recovery instance ASAP" Claude: I'll generate an emergency recovery command from your backup registry. [Uses migrate_context MCP tool for disaster recovery] 🚨 Emergency Recovery Command Generated: Copy and run this command for immediate recovery: docker run --platform linux/amd64 --network host -it --rm \ -e SOURCE_SCHEMA_REGISTRY_URL=http://backup-registry:8081 \ -e DEST_SCHEMA_REGISTRY_URL=http://dr-registry:8082 \ -e SOURCE_USERNAME=backup_user \ -e SOURCE_PASSWORD=backup_pass \ -e DEST_USERNAME=dr_user \ -e DEST_PASSWORD=dr_pass \ -e ENABLE_MIGRATION=true \ -e DRY_RUN=false \ -e PRESERVE_IDS=true \ -e DEST_IMPORT_MODE=true \ aywengo/kafka-schema-reg-migrator:latest Recovery scope: All contexts from backup registry (127 schemas) ⚠️ This is a LIVE MIGRATION - changes will be made immediately ⚠️ Monitor output for progress and any errors ⚠️ External tool provides automatic retry and parallel processing ``` **Real-Time Recovery Monitoring:** ``` Human: "How do I track the recovery progress?" Claude: Monitor the external migrator's real-time output: # The Docker command shows live recovery progress: [INFO] Starting disaster recovery... [INFO] Source: backup-registry/production [INFO] Target: dr-registry/production [INFO] Processing context: payment-events (15 schemas) [INFO] ✓ payment-created (v1-5) migrated [INFO] ✓ payment-processed (v1-3) migrated [INFO] Progress: 45/127 schemas (35%) [INFO] Estimated time remaining: 1m 30s The external migrator provides automatic retry and detailed error reporting for mission-critical recovery operations. **Post-Recovery Validation:** ``` Human: "Verify that the disaster recovery registry matches our backup" Claude: I'll verify the recovery was successful. [Uses compare_registries MCP tool] ✅ Recovery Validation Complete: Schemas matched: 127/127 Versions matched: 100% Configurations matched: 100% Disaster recovery successful! ``` **MCP Benefits:** - ✅ **Emergency Response**: Fast disaster recovery - ✅ **Parallel Migration**: Maximum speed when needed - ✅ **Continuous Monitoring**: Real-time progress updates - ✅ **Validation Tools**: Ensure complete recovery ## 🎯 Advanced Async Patterns ### 13. Scheduled Maintenance with Task Management #### **Scenario**: Planned Maintenance Window Operations Operations team needs to perform multiple maintenance tasks during a scheduled window. **Queue Multiple Operations:** ``` Human: "During tonight's maintenance window, I need to: 1) Clean up all test contexts, 2) Migrate staging to production, 3) Update all compatibility settings to FULL" Claude: I'll queue up all your maintenance operations. [Uses multiple async tools] 📋 Maintenance Tasks Queued: 1. Task ID: clean-maint-001 - Cleanup test contexts 2. Task ID: mig-maint-002 - Staging→Production migration 3. Task ID: config-maint-003 - Update compatibility settings Total estimated time: 15-20 minutes Status: Queued (will start in sequence) ``` **Monitor All Operations:** ``` Human: "Show me the status of all maintenance tasks" Claude: I'll display all your maintenance operations. [Uses list_all_active_tasks MCP tool] 📊 Maintenance Window Status: ✅ clean-maint-001: Completed (2m 15s) - Deleted 8 test contexts - Removed 67 test schemas 🔄 mig-maint-002: Running (67% - 5m elapsed) - Migrating production schemas - 89/132 schemas complete ⏳ config-maint-003: Queued - Will start after migration - Estimated duration: 30s System load: Moderate Expected completion: 11:45 PM ``` **MCP Benefits:** - ✅ **Task Orchestration**: Queue and monitor multiple operations - ✅ **Maintenance Mode**: Coordinate complex workflows - ✅ **Progress Dashboard**: Single view of all operations - ✅ **Time Estimation**: Plan maintenance windows accurately ## Schema Statistics and Monitoring ### Registry Health Monitoring Monitor the health and growth of your Schema Registry instances: ```python # Get comprehensive registry statistics stats = get_registry_statistics(registry="production") print(f"Production Registry Status:") print(f"- Total Contexts: {stats['total_contexts']}") print(f"- Total Schemas: {stats['total_schemas']}") print(f"- Total Versions: {stats['total_versions']}") print(f"- Avg Versions/Schema: {stats['avg_versions_per_schema']:.2f}") # Monitor context growth for context in stats['contexts']: print(f"\nContext: {context['name']}") print(f"- Schemas: {context['total_schemas']}") print(f"- Versions: {context['total_versions']}") ``` ### Schema Evolution Tracking Track schema evolution across different contexts: ```python # Compare schema versions across contexts dev_versions = count_schema_versions( subject="user-value", context="development" ) prod_versions = count_schema_versions( subject="user-value", context="production" ) print(f"Development: {dev_versions['total_versions']} versions") print(f"Production: {prod_versions['total_versions']} versions") print(f"Version difference: {abs(dev_versions['total_versions'] - prod_versions['total_versions'])}") ``` ### Context Management Monitor and manage contexts effectively: ```python # Get context overview contexts = count_contexts(registry="staging") print(f"Staging Registry Contexts:") for context in contexts['contexts']: schemas = count_schemas(context=context, registry="staging") print(f"\n{context}:") print(f"- Total Schemas: {schemas['total_schemas']}") print(f"- Schema List: {', '.join(schemas['schemas'])}") ``` ### Migration Planning Use statistics to plan and validate migrations: ```python # Pre-migration analysis source_stats = get_registry_statistics(registry="development") target_stats = get_registry_statistics(registry="production") print("Migration Planning:") print(f"Source (Development):") print(f"- Contexts: {source_stats['total_contexts']}") print(f"- Schemas: {source_stats['total_schemas']}") print(f"- Versions: {source_stats['total_versions']}") print(f"\nTarget (Production):") print(f"- Contexts: {target_stats['total_contexts']}") print(f"- Schemas: {target_stats['total_schemas']}") print(f"- Versions: {target_stats['total_versions']}") # Calculate migration scope contexts_to_migrate = set(source_stats['contexts']) - set(target_stats['contexts']) print(f"\nContexts to migrate: {len(contexts_to_migrate)}") ``` ### Schema Cleanup Identify unused or outdated schemas: ```python # Analyze schema versions for context in get_registry_statistics()['contexts']: print(f"\nAnalyzing {context['name']}:") for schema in context['schemas']: versions = count_schema_versions( subject=schema, context=context['name'] ) if versions['total_versions'] > 5: print(f"⚠️ {schema}: {versions['total_versions']} versions") print(f" Consider cleanup: {versions['versions']}") ``` ### Multi-Registry Comparison Compare statistics across multiple registries: ```python # Compare registry statistics registries = ["development", "staging", "production"] for registry in registries: stats = get_registry_statistics(registry=registry) print(f"\n{registry.upper()} Registry:") print(f"- Contexts: {stats['total_contexts']}") print(f"- Schemas: {stats['total_schemas']}") print(f"- Versions: {stats['total_versions']}") print(f"- Avg Versions/Schema: {stats['avg_versions_per_schema']:.2f}") ``` ### Best Practices 1. **Regular Monitoring** - Schedule regular statistics collection - Track growth trends - Set up alerts for unusual patterns 2. **Migration Planning** - Use statistics for migration scope - Validate target registry capacity - Track migration progress 3. **Cleanup Management** - Identify unused schemas - Track version history - Plan cleanup operations 4. **Performance Optimization** - Monitor schema distribution - Identify hot spots - Optimize context usage 5. **Security and Compliance** - Track schema changes - Monitor version history - Maintain audit trails --- This comprehensive use cases document demonstrates how the Kafka Schema Registry MCP Server v1.7.0 transforms schema management with async operations, multi-registry support, and intelligent progress tracking. The MCP implementation enables teams to handle large-scale operations efficiently while Claude provides real-time monitoring and guidance throughout the process.