Phase 3: AI & MCP Deep Integration (Agent Experience - AX)
Context-Aware Tools: Enhance MCP tools to provide more granular data extraction (e.g., JSONPath evaluation tool).
Generative AI: Add features to generate JSON schemas, mock data, or type definitions (TypeScript, Go, Rust) directly from the visualization using AI.
Semantic Search: Implement embedding-based search to find keys/values based on semantic meaning rather than exact string matches.
AX Optimization: Ensure all UI state changes reflect clearly in the DOM for automated browser agents.
Cross-site integrations: Keep JSON focused on JSON/schema tooling while exposing honest degraded states for future Claws, Docs, Sheets, Decks, Evals, BrowserOps, and Trace integrations.
Phase 4: Scalability & Backend Infrastructure
Large File Support: Implement virtualized rendering for the tree view to handle massive JSON files (>10MB) without performance degradation.
Streaming Parsing: Use streaming JSON parsers (like `simdjson` or custom web streams) to process data chunks iteratively.
Authentication & Accounts: Provide optional user accounts to save snippets, configurations, and history.
Edge Deployments: Optimize API routes and MCP server to run entirely on edge functions for globally low latency.
Phase 5: Community & Open Source
Comprehensive Test Suite: Achieve high test coverage across unit, integration, and E2E tests.
Developer Documentation: Expand API documentation and create tutorials/guides.
Plugin System: Develop an architecture to allow community plugins for custom visualizations or export formats.
Open Source Release: Prepare the repository for public contributions with contributing guidelines, issue templates, and CI/CD pipelines.