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DimensionOpenAI Apps SDK ExamplesOpenAI's official Apps SDK example gallery of MCP servers with interactive ChatGPT widgetsMartinLoop MCP ServerAn independent control layer that gives AI coding agents budgets, brakes, and receipts
FMRS59 / 100 · C79 / 100 · B
Reliability8 / 2012 / 20
Security and permissions13 / 2018 / 20
Maintenance12 / 2016 / 20
Documentation15 / 2018 / 20
Setup experience11 / 2015 / 20
Best for
  • Developers learning the Apps SDK/MCP component development pattern
  • Teams planning to build a custom connector app for ChatGPT
  • Engineers who want working Node and Python reference code for widget rendering and state synchronization
  • Engineering teams scaling AI coding agents while controlling spend
  • Platform teams that need auditable, rollback-aware agent execution records
  • Organizations using multiple agents (Claude, Codex, Gemini) under one governed flow
Not for
  • Anyone needing a ready-to-use production MCP service (this is example/scaffold code, not a hosted service)
  • End users who just want a specific feature without doing any development
  • Use cases requiring official support in non-ChatGPT hosts (Claude Desktop, etc.) — these examples are built around the Apps SDK/ChatGPT
  • Developers who just want a simple code-completion helper without run governance
  • Teams unwilling to accept local JSONL run records with HMAC signing
  • Environments without a supported adapter (Claude/Codex/Gemini CLIs, direct provider, or verifier-only)
Required permissions
  • Runs local HTTP servers (port 4444 by default for static assets, 8000 for the various MCP servers)
  • The Authenticated example server needs OAuth credentials configured to demonstrate authorized tool calls
  • Exposing a local server publicly via ngrok or similar requires authorizing tunnel access and configuring allowed hosts/origins
  • Read/write access to the configured repo root (writable scope restrictable via --allow-path/--deny-path)
  • Ability to invoke the chosen agent CLI (Claude/Codex/Gemini, etc.) and its underlying model account
  • Local disk writes for run records, receipts, and share outputs (share/run-receipt., etc.)
  • Execution of user-configured verifier commands (e.g. npm test)
Risks and side effects
  • The examples are learning scaffolds, not production-hardened — e.g. the shopping cart demo shows the widgetSessionId mechanism but does not persist cart state server-side
  • Exposing a local server to the internet via ngrok without restricting MCP_ALLOWED_HOSTS/MCP_ALLOWED_ORIGINS could allow unauthorized access
  • The Python Pizzaz server caches widget HTML with functools.lru_cache, so rebuilt or edited assets can appear stale until the server is restarted
  • Verifier commands are executed; poorly chosen verifier commands carry security risk despite preflight policy checks
  • Cost and token figures may be estimates (labeled with provenance) and should not be treated as settled accounting
  • Receipt integrity must be verified before use as trustworthy evidence; missing rollback evidence is flagged as EVIDENCE_BOUNDARY
  • Policy defaults come from martin.config.yaml and can be overridden by CLI flags; misconfiguration can loosen safety boundaries
Supported clientsChatGPTClaude Code, Codex, Gemini CLI, Cursor, VS Code, GitHub Copilot, Continue
Tools00