Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.
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Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.
zh_description
用于设计、构建和调试 ChatGPT Apps 与相关集成能力。
version
1.0.0
author
seaworld008
source
in-house
source_url
tags
["agent", "ai", "apps", "chatgpt"]
created_at
2026-03-04
updated_at
2026-03-20
quality
5
complexity
intermediate
ChatGPT Apps
When to Use
Use this skill when the user wants to:
build a ChatGPT app with an MCP server and widget UI
scaffold or refactor an Apps SDK project
register tools, UI resources, or bridge wiring for ChatGPT
prepare a local app for developer-mode testing or public submission
Usage
Recommended flow:
fetch current OpenAI docs
-> classify app archetype
-> plan tools before code
-> choose an example or scaffold
-> implement server and widget
-> validate local loop and deploy path
Overview
Scaffold ChatGPT Apps SDK implementations with a docs-first, example-first workflow, then generate code that follows current Apps SDK and MCP Apps bridge patterns.
Use this skill to produce:
A primary app-archetype classification and repo-shape decision
A tool plan (names, schemas, annotations, outputs)
An upstream starting-point recommendation (official example, ext-apps example, or local fallback scaffold)
An MCP server scaffold (resource registration, tool handlers, metadata)
A widget scaffold (MCP Apps bridge first, window.openai compatibility/extensions second)
A reusable Node + @modelcontextprotocol/ext-apps starter scaffold for low-dependency fallbacks
A validation report against the minimum working repo contract
Local dev and connector setup steps
A short stakeholder summary of what the app does (when requested)
Mandatory Docs-First Workflow
Use $openai-docs first whenever building or changing a ChatGPT Apps SDK app.
Invoke $openai-docs (preferred) or call the OpenAI docs MCP server directly.
Fetch current Apps SDK docs before writing code, especially (baseline pages):
apps-sdk/build/mcp-server
apps-sdk/build/chatgpt-ui
apps-sdk/build/examples
apps-sdk/plan/tools
apps-sdk/reference
Fetch apps-sdk/quickstart when scaffolding a new app or generating a first-pass implementation, and check the official examples repo/page before inventing a scaffold from scratch.
Fetch deployment/submission docs when the task includes local ChatGPT testing, hosting, or public launch:
apps-sdk/deploy
apps-sdk/deploy/submission
apps-sdk/app-submission-guidelines
Cite the docs URLs you used when explaining design choices or generated scaffolds.
Prefer current docs guidance over older repo patterns when they differ, and call out compatibility aliases explicitly.
If doc search times out or returns poor matches, fetch the canonical Apps SDK pages directly by URL and continue; do not let search failure block scaffolding.
If $openai-docs is unavailable, use:
mcp__openaiDeveloperDocs__search_openai_docs
mcp__openaiDeveloperDocs__fetch_openai_doc
Read references/apps-sdk-docs-workflow.md for suggested doc queries and a compact checklist.
Read references/app-archetypes.md to classify the request into a small number of supported app shapes before choosing examples or scaffolds.
Read references/repo-contract-and-validation.md when generating or reviewing a repo so the output stays inside a stable “working app” contract.
Read references/search-fetch-standard.md when the app is connector-like, data-only, sync-oriented, or meant to work well with company knowledge or deep research.
Read references/upstream-example-workflow.md when starting a greenfield app or when deciding whether to adapt an upstream example or use the local fallback scaffold.
Read references/window-openai-patterns.md when the task needs ChatGPT-specific widget behavior or when translating repo examples that use wrapper-specific app.* helpers.
Prompt Guidance
Use prompts that explicitly pair this skill with $openai-docs so the resulting scaffold is grounded in current docs.
Preferred prompt patterns:
Use $chatgpt-apps with $openai-docs to scaffold a ChatGPT app for <use case> with a <TS/Python> MCP server and <React/vanilla> widget.
Use $chatgpt-apps with $openai-docs to adapt the closest official Apps SDK example into a ChatGPT app for <use case>.
Use $chatgpt-apps and $openai-docs to refactor this Apps SDK demo into a production-ready structure with tool annotations, CSP, and URI versioning.
Use $chatgpt-apps with $openai-docs to plan tools first, then generate the MCP server and widget code.
When responding, ask for or infer these inputs before coding:
Use case and primary user flows
Read-only vs mutating tools
Demo vs production target
Private/internal use vs public directory submission
Before choosing examples, repo shape, or scaffolds, classify the request into one primary archetype and state it.
tool-only
vanilla-widget
react-widget
interactive-decoupled
submission-ready
Infer the archetype unless a missing detail is truly blocking. Use the archetype to choose:
whether a UI is needed at all
whether to preserve a split server/ + web/ layout
whether to prefer official OpenAI examples, ext-apps examples, or the local fallback scaffold
which validation checks matter most
whether search and fetch should be the default read-only tool surface
Read references/app-archetypes.md for the decision rubric.
Default Starting-Point Order
For greenfield apps, prefer these starting points in order:
Official OpenAI examples when a close example already matches the requested stack or interaction pattern.
Version-matched @modelcontextprotocol/ext-apps examples when the user needs a lower-level or more portable MCP Apps baseline.
scripts/scaffold_node_ext_apps.mjs only when no close example fits, the user wants a tiny Node + vanilla starter, or network access/example retrieval is undesirable.
Do not generate a large custom scaffold from scratch if a close upstream example already exists.
Copy the smallest matching example, remove unrelated demo code, then patch it to the current docs and the user request.
Build Workflow
0. Classify The App Archetype
Pick one primary archetype before planning tools or choosing a starting point.
Prefer a single primary archetype instead of mixing several.
If the request is broad, infer the smallest archetype that can still satisfy it.
Escalate to submission-ready only when the user asks for public launch, directory submission, or review-ready deployment.
Call out the chosen archetype in your response so the user can correct it early if needed.
1. Plan Tools Before Code
Define the tool surface area from user intents.
Use one job per tool.
Write tool descriptions that start with "Use this when..." behavior cues.
Make inputs explicit and machine-friendly (enums, required fields, bounds).
Decide whether each tool is data-only, render-only, or both.
Set annotations accurately (readOnlyHint, destructiveHint, openWorldHint; add idempotentHint when true).
If the app is connector-like, data-only, sync-oriented, or intended for company knowledge or deep research, default to the standard search and fetch tools instead of inventing custom read-only equivalents.
For educational/demo apps, prefer one concept per tool so the model can pick the right example cleanly.
Group demo tools by learning objective: data into the widget, widget actions back into the conversation or tools, host/layout environment signals, and lifecycle/streaming behavior.
Read references/search-fetch-standard.md when search and fetch may be relevant.
2. Choose an App Architecture
Choose the simplest structure that fits the goal.
Use a minimal demo pattern for quick prototypes, workshops, or proofs of concept.
Use a decoupled data/render pattern for production UX so the widget does not re-render on every tool call.
Prefer the decoupled pattern for non-trivial apps:
Data tools return reusable structuredContent.
Render tools attach _meta.ui.resourceUri and optional _meta["openai/outputTemplate"].
Render tool descriptions state prerequisites (for example, "Call search first").
2a. Start From An Upstream Example When One Fits
Default to upstream examples for greenfield work when they are close to the requested app.
Check the official OpenAI examples first for ChatGPT-facing apps, polished UI patterns, React components, file upload flows, modal flows, or apps that resemble the docs examples.
Use @modelcontextprotocol/ext-apps examples when the request is closer to raw MCP Apps bridge/server wiring, or when version-matched package patterns matter more than ChatGPT-specific polish.
Pick the smallest matching example and copy only the relevant files; do not transplant an entire showcase app unchanged.
After copying, reconcile the example with the current docs you fetched: tool names/descriptions, annotations, _meta.ui.*, CSP, URI versioning, and local run instructions.
State which example you chose and why in one sentence.
Read references/upstream-example-workflow.md for the selection and adaptation rubric.
2b. Use the Starter Script When a Low-Dependency Fallback Helps
Use scripts/scaffold_node_ext_apps.mjs only when the user wants a quick, greenfield Node starter and a vanilla HTML widget is acceptable, and no upstream example is a better starting point.
Run it only after fetching current docs, then reconcile the generated files with the docs you fetched.
If you choose the script instead of an upstream example, say why the fallback is better for that request.
Skip it when a close official example exists, when the user already has an existing app structure, when they need a non-Node stack, when they explicitly want React first, or when they only want a plan/review instead of code.
The script generates a minimal @modelcontextprotocol/ext-apps server plus a vanilla HTML widget that uses the MCP Apps bridge by default.
The generated widget keeps follow-up messaging on the standard ui/message bridge and only uses window.openai for optional host signals/extensions.
After running it, patch the generated output to match the current docs and the user request: adjust tool names/descriptions, annotations, resource metadata, URI versioning, and README/run instructions.
3. Scaffold the MCP Server
Generate a server that:
Registers a widget resource/template with the MCP Apps UI MIME type (text/html;profile=mcp-app) or the SDK constant (RESOURCE_MIME_TYPE) when using @modelcontextprotocol/ext-apps/server
Registers tools with clear names, schemas, titles, and descriptions
Keeps handlers idempotent or documents non-idempotent behavior explicitly
Includes tool status strings (openai/toolInvocation/*) when helpful in ChatGPT
Keep structuredContent concise. Move large or sensitive widget-only payloads to _meta.
4. Scaffold the Widget UI
Use the MCP Apps bridge first for portability, then add ChatGPT-specific window.openai APIs when they materially improve UX.
Listen for ui/notifications/tool-result (JSON-RPC over postMessage)
Render from structuredContent
Use tools/call for component-initiated tool calls
Use ui/update-model-context only when UI state should change what the model sees
Use window.openai for compatibility and extensions (file upload, modal, display mode, etc.), not as the only integration path for new apps.
API Surface Guardrails
Some examples wrap the bridge with an app object (for example, @modelcontextprotocol/ext-apps/react) and expose helper names like app.sendMessage(), app.callServerTool(), app.openLink(), or host getter methods.
Treat those wrappers as implementation details or convenience layers, not the canonical public API to teach by default.
For ChatGPT-facing guidance, prefer the current documented surface: window.openai.callTool(...), window.openai.sendFollowUpMessage(...), window.openai.openExternal(...), window.openai.requestDisplayMode(...), and direct globals like window.openai.theme, window.openai.locale, window.openai.displayMode, window.openai.toolInput, window.openai.toolOutput, window.openai.toolResponseMetadata, and window.openai.widgetState.
If you reference wrapper helpers from repo examples, map them back to the documented window.openai or MCP Apps bridge primitives and call out that the wrapper is not the normative API surface.
Use references/window-openai-patterns.md for the wrapper-to-canonical mapping and for React helper extraction patterns.
5. Add Resource Metadata and Security
Set resource metadata deliberately on the widget resource/template:
_meta.ui.csp with exact connectDomains and resourceDomains
_meta.ui.domain for app submission-ready deployments
_meta.ui.prefersBorder (or OpenAI compatibility alias when needed)
Optional openai/widgetDescription to reduce redundant narration
Avoid frameDomains unless iframe embeds are core to the product.
5a. Enforce A Minimum Working Repo Contract
Every generated repo should satisfy a small, stable contract before you consider it done.
The repo shape matches the chosen archetype.
The MCP server and tools are wired to a reachable /mcp endpoint.
Tools have clear descriptions, accurate annotations, and UI metadata where needed.
Connector-like, data-only, sync-oriented, and company-knowledge-style apps use the standard search and fetch tool shapes when relevant.
The widget uses the MCP Apps bridge correctly when a UI exists.
The repo includes enough scripts or commands for a user to run and check it locally.
The response explicitly says what validation was run and what was not run.
Read references/repo-contract-and-validation.md for the detailed checklist and validation ladder.
6. Validate the Local Loop
Validate against the minimum working repo contract, not just “did files get created.”
Run the lowest-cost checks first:
static contract review
syntax or compile checks when feasible
local /mcp health check when feasible
Then move up to runtime checks:
verify tool descriptors and widget rendering in MCP Inspector
test the app in ChatGPT developer mode through HTTPS tunneling
exercise retries and repeated tool calls to confirm idempotent behavior
check widget updates after host events and follow-up tool calls
If you are only delivering a scaffold and are not installing dependencies, still run low-cost checks and say exactly what you did not run.
Read references/repo-contract-and-validation.md for the validation ladder.
7. Connect and Test in ChatGPT (Developer Mode)
For local development, include explicit ChatGPT setup steps (not just code/run commands).
Run the MCP server locally on http://localhost:<port>/mcp
Expose the local server with a public HTTPS tunnel (for example ngrok http <port>)
Use the tunneled HTTPS URL plus /mcp path when connecting from ChatGPT
In ChatGPT, enable Developer Mode under Settings → Apps & Connectors → Advanced settings
In ChatGPT app settings, create a new app for the remote MCP server and paste the public MCP URL
Tell users to refresh the app after MCP tool/metadata changes so ChatGPT reloads the latest descriptors
Note: Some docs/screenshots still use older "connector" terminology. Prefer current product wording ("app") while acknowledging both labels when giving step-by-step instructions.
8. Plan Production Hosting and Deployment
When the user asks to deploy or prepare for launch, generate hosting guidance for the MCP server (and widget assets if hosted separately).
Host behind a stable public HTTPS endpoint (not a tunnel) with dependable TLS
Preserve low-latency streaming behavior on /mcp
Configure secrets outside the repo (environment variables / secret manager)
Add logging, request latency tracking, and error visibility for tool calls
Add basic observability (CPU, memory, request volume) and a troubleshooting path
Re-test the hosted endpoint in ChatGPT Developer Mode before submission
9. Prepare Submission and Publish (Public Apps Only)
Only include these steps when the user intends a public directory listing.
Use apps-sdk/deploy/submission for the submission flow and apps-sdk/app-submission-guidelines for review requirements
Keep private/internal apps in Developer Mode instead of submitting
Confirm org verification and Owner-role prerequisites before submission work
Ensure the MCP server uses a public production endpoint (no localhost/testing URLs) and has submission-ready CSP configured
Prepare submission artifacts: app metadata, logo/screenshots, privacy policy URL, support contact, test prompts/responses, localization info
If auth is required, include review-safe demo credentials and test the login path end-to-end
Submit for review in the Platform dashboard, monitor review status, and publish only after approval
Interactive State Guidance
Read references/interactive-state-sync-patterns.md when the app has long-lived widget state, repeated interactions, or component-initiated tool calls (for example, games, boards, maps, dashboards, editors).
Use it to choose patterns for:
State snapshots plus monotonic event tokens (stateVersion, resetCount, etc.)
Idempotent retry-safe handlers
structuredContent vs _meta partitioning
MCP Apps bridge-first update flows with optional window.openai compatibility
Decoupled data/render tool architecture for more complex interactive apps
Output Expectations
When using this skill to scaffold code, produce output in this order unless the user asks otherwise:
For direct scaffold requests, do not stop at the plan: give the brief plan, then create the files immediately.
Primary app archetype chosen and why
Tool plan and architecture choice (minimal vs decoupled)
Upstream starting point chosen (official example, ext-apps example, or local fallback scaffold) and why
Doc pages/URLs used from $openai-docs
File tree to create or modify
Implementation (server + widget)
Validation performed against the minimum working repo contract
