JSON Schema Validator MCP Server

# 🗺️ JSON Schema Validator MCP Server - Roadmap This roadmap outlines the planned features and development directions for the JSON Schema Validator MCP Server. Our goal is to create the most comprehensive and user-friendly JSON Schema validation platform for AI assistants and developers. ## 🎯 Vision To become the go-to JSON Schema validation solution that seamlessly integrates with AI assistants, provides intelligent schema generation, and supports multiple data formats with AI-enhanced capabilities. ## 📅 Development Phases ### Phase 1: Foundation (✅ Completed) - [x] Core JSON Schema Draft 2020-12 validation - [x] MCP Server implementation with stdio communication - [x] SSE Server for web clients with real-time streaming - [x] PostgreSQL database integration with file fallback - [x] Schema management (CRUD operations) - [x] External reference resolution - [x] Basic schema generation from JSON data - [x] Docker containerization ### Phase 2: Enhanced Distribution & Usability (🚧 In Progress) - [ ] **DXT Package Creation** - Following [Anthropic's DXT pattern](https://github.com/anthropics/dxt) - Create installable package for easier deployment - Simplified configuration and setup process - Pre-configured templates for common use cases - Automatic dependency management - One-command installation and configuration ### Phase 3: Advanced Schema Generation (🔄 Planned) #### Enhanced Data Input Support - [ ] **XML Data Support** - XML to JSON Schema generation - XML namespace handling - Complex XML structure analysis - XSD to JSON Schema conversion - [ ] **Class Definition Support** - Python class to JSON Schema - TypeScript interface to JSON Schema - Java POJO to JSON Schema - C# class to JSON Schema - [ ] **Database Table Support** - SQL DDL to JSON Schema - Database introspection and schema generation - Support for PostgreSQL, MySQL, SQLite - Foreign key relationship mapping - [ ] **Additional Format Support** - YAML data input - CSV with header inference - Parquet file schema extraction - Protobuf definition conversion #### Improved Schema Generation Engine - [ ] **Advanced Type Inference** - Better handling of mixed types - Date/time format detection - Email, URL, and pattern recognition - Numeric range and constraint detection - [ ] **Schema Optimization** - Redundant property elimination - Schema simplification algorithms - Performance-optimized schema generation - Configurable complexity levels ### Phase 4: AI-Powered Features (🔮 Planned) #### Intelligent Schema Creation - [ ] **AI-Enhanced Schema Generation** - Natural language to JSON Schema conversion - Context-aware schema suggestions - Automatic field description generation - Semantic relationship detection - Domain-specific schema templates - [ ] **Smart Schema Improvement** - Automatic schema validation and suggestions - Schema completeness analysis - Best practice recommendations - Performance optimization suggestions #### Example and Documentation Generation - [ ] **AI-Powered Example Generation** - Realistic sample data generation from schemas - Context-aware example values - Multiple example scenarios (valid/invalid) - Localized example data generation - [ ] **Intelligent Documentation** - Automatic schema documentation generation - API documentation from schemas - Interactive schema exploration - Visual schema representation #### Validation Intelligence - [ ] **Smart Error Messages** - Context-aware validation error explanations - Suggested fixes for validation failures - Learning from common validation patterns - Multi-language error messages ## 🚀 Near-Term Priorities (Next 3 Months) 1. **DXT Package Implementation** - Study Anthropic's DXT pattern and best practices - Create package structure and distribution mechanism - Implement automated installation and configuration - Add pre-configured templates for common scenarios 2. **Enhanced Schema Generation** - Improve JSON data analysis for better type inference - Add support for XML input format - Implement basic class definition parsing (Python/TypeScript) - Create configurable schema generation policies 3. **AI Integration Planning** - Research AI model integration options - Design architecture for AI-powered features - Create proof-of-concept for natural language schema generation - Plan example generation algorithms ## 🤝 Contributing to the Roadmap We welcome community input on our roadmap! Here's how you can contribute: ### Feature Requests - Open an issue with the `enhancement` label - Provide detailed use case descriptions - Include examples of expected behavior - Explain the business/technical value ### Priority Feedback - Comment on existing roadmap items - Share your use cases and requirements - Vote on features that matter most to you - Suggest alternative implementation approaches ### Implementation Contributions - Check out issues labeled `help-wanted` - Propose implementation designs before coding - Follow our development guidelines in [CLAUDE.md](CLAUDE.md) - Ensure comprehensive testing for new features ## 🔄 Roadmap Updates This roadmap is a living document that we update quarterly based on: - **User Feedback**: Feature requests and usage patterns - **Technical Evolution**: New technologies and standards - **Market Changes**: AI assistant platform developments - **Performance Data**: Real-world usage metrics and bottlenecks ### Latest Update: July 2025 - Added DXT package creation as immediate priority - Expanded AI-powered features section with specific capabilities - Included detailed multi-format data support plans - Added community contribution guidelines --- ## 📞 Roadmap Feedback Have thoughts on our roadmap? We'd love to hear from you: - **GitHub Issues**: Create feature requests and suggestions - **Discussions**: Join our community discussions - **Direct Feedback**: Contribute to roadmap planning sessions **Let's build the future of JSON Schema validation together!** 🚀