ROADMAP - Export Code + MCP Generation

Status: Proposed Updated: May 2026

Canonical planning note:

  • Cross-cutting MCP data access strategy is centralized in ROADMAP_MCP_UNIFIED_DATA_ACCESS.

Goal

Unify code generation entrypoints so exports can be requested from frontend actions, chatbot flows, and MCP tools while still using the same artifacts pipeline.

Scope Requested

  1. Front action to render/generate code from selected images.
  2. Integrated chatbot flow to generate code exports from conversation intent.
  3. MCP function to request new components generation from instruction text.

Product Flows

Flow A - Front Image Action

User flow:

  1. User opens pattern detail page.
  2. User selects one image from the pattern context.
  3. User clicks “Generate Code”.
  4. UI requests generation job for (patternId, imageKey, instruction?).
  5. UI shows pending state and then renders variant when ready.

Output:

  • variant TSX path under public/components/code/{patternId}/{variantId}.tsx

Flow B - Chatbot-Assisted Export

User flow:

  1. User asks chatbot for code export from a specific pattern/image intent.
  2. Chatbot resolves candidate pattern ids via search tools.
  3. Chatbot proxy calls export-generation job endpoint.
  4. Chatbot returns generation status and final export links/cards.

Flow C - MCP Instruction Tool

New MCP tool example:

  • generate_components_export

Input:

  • ids: string[]
  • instruction: string
  • imageKey?: string
  • limit?: number
  • force?: boolean

Behavior:

  • enqueue async job only
  • return jobId, normalized ids, and status URL
  • do not run heavy generation inline in edge runtime

Architecture

Shared execution engine:

  • scripts/build-artifacts-cache.mjs --mode=generate --ids=...

Runtime components:

  1. Ingress API/function endpoint
  2. Job store/queue
  3. Worker process invoking artifacts scripts
  4. Job status endpoint

Data contracts:

  • variant identity by (patternId, imageKey, promptVersion, model)
  • standardized job payload for frontend/chatbot/MCP callers

Reliability Requirements

  1. Idempotency
    • dedupe by payload hash
    • repeated same request returns same active/completed job
  2. Concurrency control
    • per-pattern lock to avoid artifact collisions
    • bounded global workers
  3. Recoverability
    • retriable transient failures
    • deterministic terminal failure reasons
  4. Cache strategy
    • if variant exists and fingerprint unchanged, return cache hit immediately
    • if cache missing, trigger build on run and return pending state

Security and Access

  1. Generation trigger can be public with rate-limits or auth depending on product phase.
  2. Export retrieval should be entitlement-aware in monetized phase.
  3. Prompt/instruction and model metadata should be logged for audit/debug.

Delivery Phases

P0 - Contracts and Queue

  • define job payload and status schema
  • implement enqueue + status endpoints
  • wire worker to artifacts script with --ids

P1 - Frontend Image Generation

  • add image selection UI action
  • call enqueue endpoint and render pending/completed states

P2 - Chatbot Integration

  • add chatbot-proxy path for generation jobs
  • return export-ready cards once artifacts are available

P3 - MCP Tooling

  • add MCP tool generate_components_export
  • enforce async job-only behavior

P4 - Monetization Controls

  • entitlement checks on export access
  • policy for free preview vs paid variants

Definition of Done

Done when all are true:

  • frontend image-based generation works end-to-end
  • chatbot can trigger and surface generated exports
  • MCP tool can enqueue instruction-based generation jobs
  • all paths converge on the same artifacts pipeline and status files