API overview

A practical map of preset AI platform API surfaces for integration work.

Platform boundary

preset AI APIs support three broad workloads:

• System data access: tokens, presets, patterns, recipes, settings
• Governance and validation: health, drift, compliance
• AI runtime integration: MCP gateway and tool-backed flows

Core surface areas

1. Design system data

Read/write APIs for canonical artifacts and metadata:

• design systems and layers
• tokens and related semantics
• presets, patterns, interactions, recipes

2. Health and compliance

APIs for quality and enforcement signals:

• health scores and audit summaries
• drift checks
• validation and confidence outputs

3. Integrations and ingestion

APIs and workflows for importing and syncing external sources:

• Figma
• codebase / repo integrations
• Storybook and ecosystem mappings

4. AI runtime access

MCP gateway and related AI service endpoints:

• discovery and constraints
• generation validation
• proposal / apply lifecycle support

Auth and access

Integrations should assume:

• authenticated requests with project-scoped identity
• role- and policy-aware access
• least-privilege for write paths
• stricter controls on governed apply operations

Read/write guidance

• default to read-first discovery
• treat writes as governed operations
• for system-level changes, use the proposal → validate → apply lifecycle

Integration checklist

Before building against the APIs, define:

• target artifacts and required scopes
• environment boundaries (local, staging, prod)
• validation expectations and failure handling
• audit and observability requirements

See Ingestion overview for how external sources flow in, and Agent resources for the MCP surface specifically.