Two platforms, two different integration models

Our AI agent deep linking guide covered the general mechanics of getting users from an AI assistant into your mobile app - custom URL schemes, deferred deep linking, and AI-readable content. This piece goes one level deeper on the two biggest assistant platforms specifically, because "get discovered by ChatGPT" and "get discovered by Claude" are not the same integration task, even though they sound like the same problem.

The short version: OpenAI's Apps SDK is about rendering an interactive UI component inside a ChatGPT conversation, built on the Model Context Protocol (MCP). Anthropic's Skills are about packaging instructions and resources that Claude loads on demand to perform a task - closer to giving Claude a procedure to follow than to rendering a UI. Building for one doesn't give you the other for free.

ChatGPT Apps SDK: in-chat UI components over MCP

The Apps SDK lets a developer register an "app" that ChatGPT can invoke mid-conversation, rendering an interactive component - a map, a booking widget, a product carousel - directly in the chat, backed by your API. Under the hood it's built on MCP: your app exposes tools and resources over an MCP server, and ChatGPT's model decides when a user's request matches one of your registered tools closely enough to invoke it.

  • What you build: an MCP server exposing typed tools (structured functions with schemas) plus optional UI components that render inside the chat surface.
  • Discovery: currently centers on explicit app selection/installation by the user and on the model matching a request to your tool's declared description and schema - not on generic web crawling.
  • What the user sees: your interactive component rendered inline in the conversation, not just a text summary or an outbound link.
  • Best fit: products where an in-chat interactive step (pick a date, browse options, confirm a booking) is genuinely better than sending the user out to your app or site.

Claude Skills: packaged instructions loaded on demand

A Claude Skill is a folder of instructions, scripts, and resources - typically a SKILL.md file plus supporting assets - that Claude can discover and load into context when a task matches what the Skill describes. Rather than rendering a UI, a Skill teaches Claude a procedure: how to fill out a specific document format, how to call a specific internal API correctly, how to follow your team's specific workflow for a recurring task.

  • What you build: a SKILL.md with a clear name and description (this is what Claude matches against), plus any scripts or reference files the skill needs.
  • Discovery: Claude scans available Skills' descriptions against the current task and loads the matching one's full instructions into context - the description quality directly determines whether your Skill gets triggered.
  • What the user sees: Claude executing the task using your Skill's instructions, often via tool calls the Skill defines - not a separate rendered UI panel.
  • Best fit: repeatable, describable procedures - especially ones involving files, structured output formats, or multi-step internal workflows - more than a visual, pick-and-click interaction.

Side-by-side comparison

Underlying protocol

Apps SDK: MCP (Model Context Protocol), a standardized way to expose tools/resources to any MCP-compatible client. Claude Skills: Anthropic's own Skills format, loaded directly by Claude - not MCP-based itself, though a Skill can call MCP tools.

What gets surfaced

Apps SDK surfaces a rendered UI component in the chat. A Skill surfaces Claude's output after following the packaged instructions - typically text, generated files, or tool-call results, not a custom visual component.

How matching happens

Both rely on the model matching a user's intent against a developer-written description - which makes that description the actual SEO surface for AI-agent discovery in both cases, even though the platforms differ.

Build effort

Apps SDK requires standing up an MCP server and, if you want the interactive experience, a UI component built to the SDK's rendering contract. A Skill can start as a well-written Markdown file with no server at all, though real ones usually include scripts.

The one thing both platforms actually reward

Despite the different mechanics, both platforms make the same underlying bet: a precise, specific description of what your tool or Skill does beats a broad, marketing-style one. "Books restaurant reservations by city, date, and party size" gets matched more reliably than "the best way to plan your night out" - because the model is pattern-matching a user's literal request against your description's literal content, not evaluating your product's positioning.

Practical implication for teams building both: write your MCP tool descriptions and your Skill's SKILL.md description with the same discipline you'd apply to a search-intent-matched page title - specific verbs, specific inputs, specific outputs. Vague, aspirational copy that reads well to a human underperforms at matching time for both platforms.

Which one should you build first

  • If the core value is a visual, interactive step a user needs to see and manipulate (dates, maps, product selection) - start with the Apps SDK.
  • If the core value is a repeatable procedure, document generation, or internal-workflow automation with no strong UI need - start with a Skill.
  • If you're not sure yet, a Skill is typically the faster, lower-commitment way to test whether AI-assistant-driven usage of your product is worth the investment, before building a full MCP server and UI component.
  • Neither replaces the other long-term if your product genuinely has both a visual interaction and a repeatable-procedure use case - larger products often end up building for both platforms independently.

Key takeaways

  • Apps SDK (ChatGPT) renders interactive UI components in-chat over MCP; Claude Skills package instructions Claude loads on demand - different mechanics, not interchangeable integrations.
  • Both platforms match user intent against a developer-written description, making that description the real discovery surface on both platforms.
  • A Skill is generally the lower-effort way to start if you're testing whether AI-assistant traffic matters for your product before committing to a full MCP server build.
  • Write tool and Skill descriptions with search-intent-level specificity, not marketing copy - vague descriptions underperform at matching time regardless of platform.
  • Larger products with both a visual-interaction use case and a repeatable-procedure use case often need to build for both platforms, not pick one permanently.
Deciding between an MCP-based Apps SDK integration and a Claude Skill for your product? That's a scoping conversation worth having before either build starts.