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A coding agent added Jetpack Compose rendering to Evgeny Khramov’s local Android UI Renderer MCP without requiring a project-specific adapter. In his account, the work covered research, implementation, debugging, tests, and a demo. The important constraint was not simply producing a screenshot: the renderer also had to return a machine-readable structure an agent could inspect. Khramov describes the result and its limits in his case study.
What the Android UI Renderer MCP does
Khramov describes Android UI Renderer MCP as a local server that renders Android UI without an emulator or physical device. It returns a PNG and a component tree that an agent can query for details such as bounds, text, and state. Before Compose support, it handled conventional Android UI, including XML and View layouts, RecyclerView, Activity and Fragment content, overlays, and screen configurations.
The new render_compose tool extends that workflow to Jetpack Compose. Its goal is to render a particular composable for inspection, not to run an entire application as a user would.
How render_compose works
The caller provides the fully qualified name of a top-level composable, named JSON arguments for that function, and rendering dimensions and density. The implementation described by Khramov then:
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- Locates the Kotlin function and parses its signature.
- Checks the supplied arguments and constructs typed values, including supported forms such as nullable values, enums, data classes, mutable properties, lists, and sets.
- Generates a Kotlin call, supplying no-op lambdas for callbacks that were not provided.
- Resolves an app theme or uses an explicit wrapper.
- Creates a temporary Kotlin probe under
.android-ui-rendererand runs it with Gradle and Robolectric. - Draws a
ComposeViewand returns a PNG along with a Compose semantics tree.
The stated design keeps the project’s own source files unchanged. It also assumes the composable’s visual state can be supplied explicitly as arguments. That makes a single component or state straightforward to render, but it does not make navigation, dependency injection, or live data loading part of the described path.
Why the first screenshot was not enough
The first real Compose render produced an image, but serialization failed afterward: a Compose semantics node did not have the className field expected by the renderer’s existing protocol. The implementation added a fallback class name and emitted the Compose semantics structure, including bounds, text, content description, enabled, clickable, selected, and checked state, plus child nodes.
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That failure exposed an important distinction. A screenshot is useful to a person, but an agent using this tool also needs queryable structure. The Compose semantics tree provides that structure; it is not an Android View tree, and its IDs are synthetic.
What the author checked
For a broader check than a toy composable, Khramov says he had the agent clone an open-source Jetpack Compose sample and create a parallel sample-compose/ app. The two apps shared a book library and seven scenarios:
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- A long title at font scale 1.3
- A list
- A master-detail view
- A full-screen view
- A loading state
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According to the case study, both stacks were captured at 1920×1200 pixels and 240 dpi after a follow-up corrected an earlier canvas-size mismatch. Khramov is explicit that this was not a pixel-perfect XML-versus-Compose comparison: it showed equivalent states rendered through different mechanisms. He also reports unit tests for enums, data classes, mutable state, and callbacks, as well as a real Compose component run and the demo scenarios. These are the author’s reported checks, not independently reproduced results.
A separate Gradle problem affected the demo
The sample’s build issue was distinct from Compose rendering. The renderer injects test dependencies through a Gradle init script, but the sample project’s strict repository policy blocked adding the temporary Robolectric dependency that way. The reported fix was to change the sample’s repository mode to PREFER_PROJECT and add a comment explaining the need for the temporary test dependency. This was a project configuration issue, not a Compose API failure.
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What the coding agent did—and what the author did
Khramov’s stated division of labor was that he specified a property the result had to satisfy and let the agent handle the research and implementation. He says the agent also worked through debugging, tests, and a reproducible demonstration; his account does not mean the technical difficulty disappeared. The semantics-tree failure and Gradle repository-policy problem both required the implementation to satisfy conditions beyond generating a basic image.
The timeline is project-specific and self-reported. Khramov’s account places his first note at 12:26, a rejected mandatory project-side entry point at 12:41, the first real render at about 13:00, the main commit at 14:05, and a follow-up at 14:09—about 1 hour 43 minutes from first note to follow-up. He expressly does not present this as a comparison with how long a person would take; he says he probably would not have started the research himself. That is an account of one task, not a general productivity benchmark.
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Limits of the implementation
- It renders a component, not a production flow. The target is a top-level composable, not full navigation, dependency injection, or live data loading.
- It does not simulate user interaction. Visual state comes from supplied arguments, and missing callbacks become no-op lambdas.
- Its signature parser has edge cases. The implementation uses a custom regex-based parser rather than the Kotlin compiler API. Khramov identifies complex overloads, type aliases, and unusual annotations as areas that may require more work.
- Its structure is Compose semantics. Consumers should not assume an Android View hierarchy or stable, application-defined IDs.
- Physical-device fidelity is unverified. The author does not claim pixel-perfect agreement with a real device.
How this differs from Google’s Android agent capabilities
This project is a developer tool for rendering and inspecting an app’s existing Compose UI. Google’s Android AI overview describes separate capabilities: AppFunctions, a platform API with a Jetpack library for exposing discrete app capabilities that agents can discover and execute; remote MCP servers as a complementary server-side integration path; and A2UI, which uses structured agent-to-UI messages rendered as native Jetpack Compose and Material 3 components.
Those technologies address different roles. The case study’s local renderer gives an agent a way to inspect a developer’s UI; AppFunctions exposes app actions, remote MCP connects server-side capabilities, and A2UI concerns agent-generated interfaces. The sources do not establish that the renderer is part of Google’s APIs or that these systems are interchangeable.
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