What changed: GPTs vs. Apps (source-backed)

• Surface & UX
  • GPTs: conversational only; no custom UI beyond messages. GPT Store distribution.
  • Apps: UI components (cards, carousels, fullscreen) rendered inline in ChatGPT; model chooses when to invoke your tools based on metadata.
• Integration model
  • GPTs: configuration (instructions), knowledge files, and Actions for external APIs.
  • Apps: your MCP server exposes typed tools; the Apps SDK sends structured results + UI.
• Governance & safety
  • GPTs: publish/feature via Store; follow GPT policies.
  • Apps: App developer guidelines require write-action labeling and human confirmation for state-changing/egress actions; follow Security & Privacy (least privilege, consent, logging).
• Availability (Oct 2025)
  • Apps: preview; submissions later this year; not yet on Business/Enterprise/Edu clients.
  • GPTs: available and publishable today via GPT Store.

Should you migrate? A quick decision rubric

Move a GPT to an App when you need any of the following:

1. Actionable UI (lists, review/confirm flows, multi-step widgets) instead of pure text.
2. Write actions that must be explicitly confirmed by the user (create/update/delete, posting or sending data).
3. Governed access to first-party systems via MCP with narrow tool contracts and least-privilege scopes.
4. Discovery tuning using metadata/golden prompts to increase precision/recall for high-intent queries.
5. Future distribution via the upcoming app directory/monetization, beyond the GPT Store.

Keep a GPT (or run dual-track) when:

• The experience is conversation-only, no state changes, and Store presence is sufficient.
• You rely heavily on knowledge files with light retrieval; Apps can also do this via File Search + vector stores, but a GPT might remain simpler.

Migration playbook (contract-first, tool-first)

Step 1 — Inventory your GPTs
Capture for each GPT: instructions, knowledge files, Actions/OpenAPI, auth, target audience, KPIs. (Builder & publishing docs outline what GPTs contain.)

Step 2 — Map features → Apps primitives

• Instructions → Tool metadata + server logic (move behavioral rules into narrow tool definitions and server validation; keep “when to use” phrasing in metadata).
• Actions/OpenAPI → MCP tools with read vs write separation (label writes for confirmation).
• Knowledge files → Responses API File Search over vector stores (upload → embed → retrieve).
• Chat outputs → Apps UI components: list → detail → review/confirm; keep fullscreen for deep review only.

Step 3 — Stand up an MCP server
Expose narrow, JSON-schema tools (e.g., orders.lookup, tickets.create). Use MCP Inspector to validate inputs/outputs before UI polish.

Step 4 — Build the App UI
Render inline components in an iframe; connect via window.openai. Keep forms minimal and 1:1 with tool parameters.

Step 5 — Connect & test in ChatGPT
Enable Developer Mode, Connect from ChatGPT, then run your golden prompts to measure discovery precision/recall.

Step 6 — Compliance hardening
Apply App developer guidelines (write-action labels, accurate metadata, privacy policy) and Security & Privacy (least privilege, explicit consent, input validation, logging).

Step 7 — Prepare for submission (when the queue opens later this year)
Finalize metadata, screenshots, and support contact per guidelines. Note that submissions aren’t open yet; plan content now.

Scoping worksheet (use this in procurement)

• Core tasks (ranked): e.g., quote a policy, generate an SOW, refund an order.
• Tools (read vs. write): keep one job per tool; enumerate inputs/outputs.
• UI plan: the fewest components to complete each task; when (if ever) to switch to fullscreen.
• Discovery metadata: app name, descriptions (“Use this when…”), parameter docs; build your golden-prompt set now.
• Data & auth: what data is required; File Search stores; OAuth/SSO; scope boundaries.
• Risk controls: label all write actions; server-side validation; retention & logging policy.

Timelines & resourcing (typical)

• 2–3 weeks: contracts + MCP server + minimal components; tested end-to-end in Developer Mode.
• 2–3 weeks: File Search integration (if needed), auth, discovery tuning with golden prompts.
• 1–2 weeks: compliance hardening and submission assets (when open).

KPIs to track post-migration

• Discovery precision/recall on your prompt set (Apps choose tools from metadata).
• Write-action confirmation rate & error rate (signal of trust/clarity).
• Answer groundedness when using File Search (results/citations returned by vector stores).
• Time-to-task completion and abandonment after UI render (optimize component weight).

Risks & gotchas (and mitigations)

• Assuming Apps run on Enterprise/Edu today. They don’t (client-side); plan internal rollouts via MCP connectors meanwhile.
• Over-broad tools. Kitchen-sink endpoints harm discovery and safety; split into single-purpose read/write tools.
• Skipping write-action labels. This violates policy and removes confirmation UX; label every state-changing/egress action.
• Treating retrieval as magic. Use vector stores and test search quality; don’t dump raw corpora.

Why hire us for migration

We deliver contract-first MCP backends, Apps SDK UI that converts, and submission-ready governance: write-action labeling, least-privilege scopes, and File Search integration. Our process maps 1:1 to OpenAI’s Apps SDK docs, App developer guidelines, and Security & Privacy guidance—so your legacy GPTs become production-grade Apps with measurable lift.