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Complete user guides for Data Generation, API Mocking, and MCP integration, organized for both developers and LLM-driven workflows.

Basics

Limits and Plans

Understand plan limits and usage-based behavior across features.

Limits and Plans

Plan limits affect generation volume, API usage, and mocking capacity.

Common Limit Categories

  • AI token usage
  • Number of schemas/databases
  • Rows per table
  • Tables per schema
  • API request limits
  • Number of mocks
  • Endpoints per mock
  • Scenarios per endpoint
  • Mock request limits

How Limits Affect Behavior

Data Generation

  • Large row targets can be capped by plan limits.
  • AI-assisted mapping can consume token quotas.
  • Generation requests may fail when limits are exhausted.

API Mocking

  • New mock creation can stop at plan maximum.
  • Endpoint and scenario creation can be constrained.
  • High request volume may trigger throttling or rejection.

MCP

  • MCP tool calls follow account permissions and usage quotas.
  • Tool actions that invoke AI can consume token limits.

Practical Guidance

  • Start with realistic row counts for core tables first.
  • Avoid over-indexing endpoint scenarios early in setup.
  • Reuse codebase identifiers in MCP to avoid duplicate artifacts.
  • Monitor usage periodically before long generation sessions.

Upgrade Strategy

  • Increase limits when generation or mocking workflows are blocked by quotas.
  • Keep environment-level usage dashboards visible during test cycles.
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