Connect your PlanetScale database to Basedash to visualize and analyze your application data. The easiest way to connect is by using the connection string (URI) method. In Basedash, press Command + K and paste your connection string to start the connection flow immediately. Example: 
mysql://[USERNAME]:[PASSWORD]@[HOST]/[DATABASE]?sslmode=require
Note: The password is not always visible in your database dashboard. Make sure you have access to it—this may be stored in a password manager like 1Password or LastPass, or you may need to contact a teammate who has the credentials.
Tip: Labels and connection methods may differ depending on your hosting provider (GCP, AWS, DigitalOcean, etc.). Always check your provider’s documentation for the latest info.

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Manual connection details

If you prefer to enter credentials manually, use the following details:
  1. From your Basedash dashboard, click “Add Data Source”
  2. Select “PlanetScale” as your database type
  3. Enter your connection details:
    • Host (from PlanetScale connection string)
    • Database name
    • Username
    • Password
    • SSL mode (required)
  4. Click “Test Connection” to verify
  5. Save your connection

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Required permissions

  • Database password with read access
  • Access to the desired branch (main or development)
  • Appropriate organization permissions

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Best practices

  • Use a dedicated password for Basedash
  • Connect to the appropriate branch
  • Enable SSL for secure connections
  • Regularly rotate database passwords
  • Monitor query performance

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Performance optimization

  • Use appropriate indexes
  • Monitor slow queries
  • Set up proper schema design
  • Configure connection pools
  • Use query caching when possible

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Troubleshooting

  • Verify database password is active
  • Check branch availability
  • Monitor connection limits
  • Review query logs
  • Validate SSL configuration

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Next steps: Add custom context

You can add custom context to help the AI better understand your data structure and business logic. Consider adding context at the database or schema level if you notice the AI struggling to locate or understand specific data.

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When to add context

  • Complex JSON columns: When the AI needs help understanding nested data structures
  • Unclear naming conventions: If table or column names don’t clearly indicate their purpose
  • Business-specific terminology: When your data uses internal abbreviations or custom terms
For detailed guidance, see our custom context documentation.