PerchIQX Playground
Try out PerchIQX's ICE (Insight-Context-Execution) methodology with real database intelligence scenarios. Each example demonstrates how schema analysis, validation, and comparison work together to provide actionable insights with mathematically-derived priorities.
🎯 PerchIQX Playground
Experience ICE methodology in action with real database scenarios
Schema Drift Detection
Compare development and production schemas to detect drift with ICE-scored differences
Pre-Deployment Validation
Ensure staging matches production before deployment to prevent schema conflicts
Performance Optimization Analysis
Identify missing indexes and optimization opportunities with ICE-scored recommendations
Schema Health Check
Validate database schema for anti-patterns and structural issues
Complete Database Analysis
Deep dive into schema structure with relationships, indexes, and sample data
About These Examples
All scenarios above are based on actual MCP tool implementations:
- Real tool schemas from the PerchIQX codebase
- Authentic ICE scoring (multiplicative algorithm with observable anchors)
- Production-ready responses matching actual tool output format
- Complete schema analysis including tables, columns, indexes, and foreign keys
What You're Seeing:
- User Query - Natural language database question
- Tool Invocation - MCP server selects appropriate tool and parameters
- Tool Result - Schema analysis with ICE-scored recommendations
- PerchIQX Response - Actionable insights with derived priorities
Key Features Demonstrated:
- 🎯 ICE Methodology - Insight × Context × Execution scoring (not hardcoded priorities)
- 📊 Observable Anchoring - Decisions based on directly measurable schema properties
- 🔍 Schema Drift Detection - Compare environments with ICE-scored differences
- ⚡ Multi-Tool Composition - Tools working together for complete intelligence
- 🚀 Production-Ready - Real migration plans and optimization recommendations
ICE Score Breakdown:
Every recommendation includes full ICE analysis:
- Insight (I): 0-10 - Semantic depth and business impact
- Context (C): 0-10 - Environmental criticality (production=10, dev=4)
- Execution (E): 0-10 - Action clarity and implementation ease
- Combined Score:
(I × C × E) / 100→ Automatic priority derivation
Priority Thresholds:
- High: ≥ 6.0 (immediate attention required)
- Medium: 3.0-5.9 (plan for next release)
- Low: < 3.0 (technical debt, nice-to-have)
Want to Try the Real Thing?
To use PerchIQX with your own Cloudflare D1 databases:
- Install Claude Desktop - Required for MCP integration
- Get Cloudflare API Access - Account ID + API Token
- Clone the Repo - Open source on GitHub
- Follow Setup Guide - 5-minute configuration
Real-World Use Cases
🚀 Pre-Deployment Validation
Use compare_schemas to ensure staging matches production before deployment:
// Detect critical differences before going live
compare_schemas({
sourceDatabaseId: "staging-db",
sourceEnvironment: "staging",
targetDatabaseId: "prod-db",
targetEnvironment: "production"
})🔍 Continuous Drift Monitoring
Set up automated schema drift detection in your CI/CD pipeline:
# GitHub Actions example
- name: Check Schema Drift
run: npx perchiqx compare-schemas --source staging --target prod⚡ Performance Optimization
Identify missing indexes and optimization opportunities:
// Get ICE-scored optimization recommendations
suggest_database_optimizations({
environment: "production"
})🛡️ Schema Validation
Catch anti-patterns and structural issues before they cause problems:
// Validate schema health and best practices
validate_database_schema({
environment: "production"
})Technical Deep Dive
Interested in how ICE methodology works under the hood?
- ICE Methodology - Complete scoring algorithm explanation
- Architecture - Hexagonal architecture and domain design
- Compare Schemas - Comprehensive drift detection guide
- Tools Overview - All 5 MCP tools documented
- The Perch Metaphor - Understanding the intelligence perspective