Schemaflow MCP Server
The SchemaFlow MCP Server provides real-time PostgreSQL and Supabase database schema access for AI-IDE, enabling real-time schema context for intelligent code generation through the Model Context Protocol.
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What is the SchemaFlow MCP Server?
The SchemaFlow MCP server is a tool that connects AI-IDE and PostgreSQL/Supabase databases through the Model Context Protocol (MCP). It allows AI assistants to obtain real-time database structure information, thereby improving code generation efficiency.How to use the SchemaFlow MCP Server?
Users only need to connect to the database on the SchemaFlow dashboard and generate an MCP token, then configure it in an AI-IDE that supports MCP to use it.Applicable Scenarios
Suitable for AI-IDE development environments that require real-time database context, such as Cursor, Windsurf, and VS Code + Cline, etc., to help developers write database-related code more efficiently.Main Features
Real-time Database AccessAllows AI-IDE to obtain real-time structure information of PostgreSQL and Supabase databases, including tables, columns, relationships, functions, etc.
Secure Token AuthenticationAuthenticates through a unique and revocable MCP token to ensure the security of data access.
Multi-platform CompatibilitySupports mainstream AI-IDs such as Cursor, Windsurf, and VS Code + Cline for seamless integration.
Database AnalysisProvides functions such as database performance analysis, structure evaluation, and security checks to help optimize database design.
Advantages and Limitations
Advantages
Improve the accuracy of AI-IDE code generation
Simplify the database interaction process
Support multiple mainstream AI-IDs
Ensure the security of data access
Limitations
Limited to PostgreSQL and Supabase databases
Need to set up the database on the SchemaFlow dashboard first
Depend on network connection to access the MCP server
How to Use
Get an MCP Token
Visit the SchemaFlow dashboard, connect your PostgreSQL or Supabase database, and generate an MCP token.
Configure the AI-IDE
According to the AI-IDE you use (such as Cursor, Windsurf, or VS Code + Cline), add the MCP server configuration as instructed.
Start Using
After the configuration is completed, the AI-IDE will be able to access your database structure in real-time to help you write more accurate code.
Usage Examples
View the Database StructureWhen developers need to understand the structure of the current database, they can use the `get_schema` command to obtain information about all tables and columns.
Analyze the Database PerformanceThe AI-IDE can call `analyze_database` to identify potential performance issues and provide optimization suggestions.
Verify the Database StructureUse the `check_schema_alignment` command to ensure that the database structure conforms to best practices and avoid naming convention and design issues.
Frequently Asked Questions
Which databases does the SchemaFlow MCP Server support?
How to get an MCP token?
Is the MCP server secure?
In which AI-IDs can I use the SchemaFlow MCP Server?
Related Resources
SchemaFlow MCP Guide
Detailed documentation on MCP integration and configuration
SchemaFlow Dashboard
A web interface for connecting to the database and managing MCP tokens
SchemaFlow GitHub Repository
Source code and development documentation for the MCP server
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