SchemaFlow

Product Introduction

1. SchemaFlow is a schema management platform that connects PostgreSQL and Supabase databases to AI-powered development environments (AI-IDEs) through the Model Context Protocol (MCP). It enables real-time schema synchronization, interactive visualization, and multi-format exports to provide AI assistants with accurate database context for code generation. The platform operates as a middleware layer between databases and AI-IDEs like Cursor, Windsurf, and VS Code extensions.
2. The core value of SchemaFlow lies in bridging the gap between database schemas and AI-driven development tools by maintaining live schema context. It ensures AI assistants generate code aligned with the latest database structure while offering tools for schema analysis, validation, and team collaboration. This reduces errors caused by outdated schema references and accelerates development workflows.

Main Features

1. SchemaFlow provides real-time schema synchronization via Model Context Protocol (MCP) using Server-Sent Events (SSE), enabling AI-IDEs to access live database structures without manual refreshes. This integration supports tools like Cursor and Windsurf with secure token authentication for direct IDE connections.
2. The platform offers interactive schema visualization tools, including relationship maps and dynamic diagrams, to explore table structures, constraints, and dependencies. Users can validate schema changes against AI-generated code before deployment, ensuring compatibility with the actual database.
3. Multi-format schema exports are available in JSON, Markdown, SQL, and Mermaid formats for documentation, AI training, and team collaboration. These exports are optimized for AI-IDE compatibility, enabling seamless integration with code generation pipelines and version control systems.

Problems Solved

1. SchemaFlow addresses the "AI context gap" where AI-IDEs lack real-time access to database schemas, leading to inaccurate code suggestions based on stale or incomplete schema data. Manual schema sharing via static exports creates versioning issues and delays in AI-assisted workflows.
2. The product targets development teams using AI-powered IDEs like Cursor or GitHub Copilot that require up-to-date schema context for code generation. It is particularly relevant for PostgreSQL/Supabase users working on applications with complex or frequently updated database structures.
3. Typical use cases include providing real-time schema context to AI assistants during feature development, generating documentation for cross-functional teams, and analyzing schema relationships during database refactoring. It also supports secure schema sharing with external AI tools without exposing raw database credentials.

Unique Advantages

1. Unlike static schema export tools, SchemaFlow combines live MCP integration with visualization capabilities, offering both real-time access and exploratory analysis in one platform. Competitors typically focus on either documentation or IDE plugins, not protocol-level synchronization.
2. The SSE-based MCP implementation ensures low-latency schema updates to AI-IDEs, with automatic caching to maintain availability during database downtime. Secure token authentication replaces traditional API keys, enabling user-specific access control without compromising database security.
3. Competitive advantages include native Supabase compatibility, AI-optimized export formats, and beta support for schema validation within IDE workflows. The platform’s focus on PostgreSQL ecosystems allows deeper integration with extensions and advanced database features compared to generic tools.

Frequently Asked Questions (FAQ)

1. What databases and versions does SchemaFlow support? SchemaFlow currently supports PostgreSQL 12+ and Supabase databases, focusing on the public schema by default. Extended support for private schemas and other database systems is planned for future releases.
2. What export formats does SchemaFlow offer? The platform provides JSON for AI training, Markdown for documentation, SQL for migration scripts, and Mermaid for diagram embedding. All formats include metadata about tables, columns, indexes, and relationships.
3. How do I set up MCP with my AI-IDE like Cursor? Generate an MCP token in SchemaFlow’s dashboard, then configure your IDE using the provided SSE endpoint URL and token. The platform’s documentation includes step-by-step guides for major AI-IDEs with automatic schema polling every 15 seconds.