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

MCP

MCP Workflows

Common end-to-end agent workflows for generation and mocking.

MCP Workflows

These workflows show practical, repeatable agent-driven operations.

Workflow 1: New Codebase Generation

  1. Derive codebase identifier from git remote.
  2. Call generate-fake-data with full schema.
  3. Poll until completion.
  4. Retrieve generated result references.

Workflow 2: Evolving Existing Schema

  1. Call get-codebase-schema with codebase identifier.
  2. Add newly introduced tables.
  3. Add/remove changed columns.
  4. Regenerate fake data.

Workflow 3: Mock API Bootstrap

  1. Call create-api-mock with domain prompt.
  2. Review generated endpoints.
  3. Call get-api-mock for exact endpoint/scenario details.
  4. Iterate prompt for additional coverage.

Workflow 4: Extend Existing Mock

  1. Call list-mocks.
  2. Pick mock by ID.
  3. Call create-api-mock with mock_id and prompt for only new endpoints.
  4. Validate responses in client tests.

Workflow 5: Safety-First Operation Pattern

  1. Start with read-only tools (list/get).
  2. Validate context and identifiers.
  3. Use mutation tools intentionally.
  4. Re-read state and confirm expected changes.

Team Conventions

  • Store shared codebase identifiers in project docs.
  • Keep MCP prompts deterministic and explicit.
  • Prefer incremental mutations over large one-shot changes.
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