schema-lint-mcp
Validates schema files against customizable lint rules using Claude or Gemini AI, supporting JSON and SQL schemas with rules for naming, structure, and migration safety.
README
Schema Lint MCP
An MCP server that validates schema files against lint rules using Claude or Gemini AI.
Installation
- Clone this repository
- Install dependencies:
npm install - Build the project:
npm run build
Configure with Claude Code
Add this to your Claude Code settings JSON file:
{
"mcpServers": {
"schema-lint": {
"command": "node",
"args": ["/path/to/schema-lint-mcp/dist/index.js"],
"env": {
"ANTHROPIC_API_KEY": "your-anthropic-api-key-here",
"GEMINI_API_KEY": "your-gemini-api-key-here"
}
}
}
}
Or use environment variables:
{
"mcpServers": {
"schema-lint": {
"command": "node",
"args": ["/path/to/schema-lint-mcp/dist/index.js"],
"env": {
"ANTHROPIC_API_KEY": "${env:ANTHROPIC_API_KEY}",
"GEMINI_API_KEY": "${env:GEMINI_API_KEY}"
}
}
}
}
Usage
Once configured, you can use the validate_schema tool in Claude Code:
// Using Claude (default)
validate_schema(schemaPath: "/path/to/schema.json", rulesPath: "/path/to/rules.json")
// Using Gemini
validate_schema(schemaPath: "/path/to/schema.json", rulesPath: "/path/to/rules.json", provider: "gemini")
Example Files
The examples/ directory contains sample schemas and rule sets:
Schema Files
schema.json- Example JSON schemaschema.sql- Example SQL schema with tables and relationships
Rule Sets
rules.json- Basic JSON schema validation rulessql-rules.json- General SQL best practicesschema-rules.json- Database schema structure and naming conventions (inspired by Bytebase)migration-rules.json- Database migration safety and compatibility rules (inspired by Bytebase)
Rule Categories
Schema Rules focus on:
- Naming conventions (tables, columns, indexes)
- Table structure requirements (primary keys, constraints)
- Column definitions (types, defaults, nullability)
- Index optimization
- Documentation (comments)
Migration Rules focus on:
- Backward compatibility
- DDL safety (avoiding table locks, data loss)
- DML safety (WHERE clauses, row limits)
- Performance considerations
- Migration hygiene (separation of concerns)
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