Why task-specific support apps (not one giant “agent”)

Support journeys break into discrete, high-intent tasks—reset a password, check an order, create a ticket, authorize a refund. In the Apps SDK, each task becomes a small set of MCP tools (typed JSON-schema endpoints) plus a focused UI rendered in the conversation. This keeps the model’s tool calls predictable, the UI fast, and security reviews tractable.

What you can build (customer-service playbooks)

1) Tier-0 self-service & deflection

Tools: lookup_order(order_id), reset_password(user_id), get_return_label(order_id)
UI: compact list/detail with a one-step confirm for any action that changes state. Mark these as write actions so ChatGPT inserts a human confirmation modal.

2) Agent-assist inside ChatGPT

Tools: summarize_thread(url), suggest_reply(tone, constraints), create_ticket(subject, body, priority)
Flow: show a threaded summary and an editable reply; on Create ticket, require confirmation (write). Test via Developer Mode before rollout.

3) Knowledge lookup (RAG) for policy answers

Option A (Apps SDK + MCP): expose your internal KB as read tools and render results inline.
Option B (backend services): pair your product’s backend with Responses API tools like File Search (for org-managed docs) or Web Search when policy allows. Use Apps SDK as the user surface.

Architecture at a glance

ChatGPT UI (Apps SDK) renders inline components (cards, forms, review screens) inside the chat; components are usually React, run in a sandboxed iframe, and talk to the host via window.openai.
Your MCP server exposes narrowly scoped tools (read/write) with JSON Schemas and returns structured results (and optional component metadata).
Downstream systems (CRM, ticketing, auth) are accessed with least-privilege scopes; write actions trigger explicit confirmations in ChatGPT.

Distribution & internal deployment options

Public apps in ChatGPT (preview): discoverable in-chat; submissions open later this year; EU expansion planned.
Private, internal connectors (full MCP beta): Admins on Business/Enterprise/Edu can enable Developer Mode, create and publish custom MCP connectors with write/modify actions; OpenAI-built connectors are search-only today. (Apps UI isn’t available on Business/Enterprise/Edu yet.)

UX patterns that convert in support

Inline first, fullscreen sparingly. Keep the composer present; use fullscreen only to review/confirm high-stakes actions.
Mirror the schema. Every form field should map to a tool parameter; don’t collect extra data.
Name tools for discovery. Treat metadata like product copy: Use this when… in descriptions, clear parameter docs—this increases recall and reduces false activations.

Security, privacy & governance (what reviewers will ask)

Least privilege + explicit consent. Request only necessary scopes; show clear permission prompts; always label write actions so the client can require confirmation.
Sensitive data bans & data minimization. Don’t collect PCI, PHI, government IDs, API keys, or passwords; publish a privacy policy and retention plan.
Sandboxed components & CSP. Components run isolated in an iframe; follow OpenAI’s custom-UX guidance.

Build & rollout plan (4–6 weeks, scope-dependent)

Phase 1 — POC (Dev Mode)

Define 3–5 contract-first tools (e.g., lookup, create_ticket, update_case).
Ship minimal components (list → detail → confirm). Test in Developer Mode and the API Playground (connect your HTTPS /mcp endpoint).

Phase 2 — Alpha with auth & KB

Add OAuth/OIDC if the app links accounts or reads private data; verify scopes per call.
Optional: integrate org KB via Responses: File Search behind your service boundary.

Phase 3 — Hardening & submission readiness

Apply the App developer guidelines (sensitive-data rules, accurate write-action labels, support contact).
Run golden prompts to measure discovery precision/recall before publishing/submit.

KPIs to instrument from day one

Containment/deflection rate (resolved without human handoff) and write-action confirmation rate (signals trust & clarity).
Discovery precision/recall for target prompts (track with your golden-prompt set).
Time to first response / resolution and policy adherence (guarded by confirmation steps + least privilege).

RFP checklist (use with vendors)

Tool contracts (JSON Schemas) for each support task; clear read vs write separation.
Metadata strategy (“Use this when…”, parameter docs) + a golden-prompt test plan.
Security & Privacy mapping: scopes, consent flows, confirmation UX, privacy policy, retention.
Testing evidence: Developer Mode runs, API Playground traces, and mobile checks.

Why partner with us

We specialize in task-specific support apps that pass InfoSec review: contract-first MCP tools, native in-chat UI, least-privilege auth, explicit write-action confirmations, and a discovery optimization plan (metadata + golden prompts). Our delivery maps 1:1 to OpenAI’s Apps SDK, Security & Privacy, and Developer Mode guidance—so your support automation ships fast and safely.

Customer Support Automation: Building Task-Specific ChatGPT Apps with the Apps SDK