SceneView MCP

3D ve AR geliştirme için 22 araç — Android (Jetpack Compose) ve iOS (SwiftUI) için doğru, derlenebilir SceneView kodu üretir. 858 test.

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sceneview-mcp

Give any AI assistant expert-level knowledge of 3D and AR development.

The official Model Context Protocol server for SceneView — the cross-platform 3D & AR SDK for Android (Jetpack Compose + Filament), iOS / macOS / visionOS (SwiftUI + RealityKit), and Web (Filament.js + WebXR).

Connect it to Claude Code, Cline, Codex, Cursor, GitHub Copilot, JetBrains AI Assistant — or any other MCP client — locally over stdio, or remotely over Streamable HTTP at https://mcp.sceneview.dev/mcp (see Remote server). Your AI assistant gets specialized tools, compilable code samples, the full API reference, a code validator, and an inline 3D viewer widget — so it writes correct, working 3D/AR code on the first try.

Disclaimer: Generated code is provided "as is" without warranty. Always review before production use. See TERMS.md and PRIVACY.md.

Quick start

One command — no install required:

npx sceneview-mcp

Every client below runs that same server. Three config shapes exist across the ecosystem — mcpServers (most clients), servers (VS Code) and [mcp_servers.*] (Codex TOML) — but the command and arguments are identical in all three.

Any MCP client

{
  "mcpServers": {
    "sceneview": {
      "command": "npx",
      "args": ["-y", "sceneview-mcp"]
    }
  }
}

Claude Code

claude mcp add sceneview -- npx -y sceneview-mcp

Or commit .mcp.json at the repository root so the whole team gets it:

{ "mcpServers": { "sceneview": { "type": "stdio", "command": "npx", "args": ["-y", "sceneview-mcp"] } } }

Optionally, the SceneView Claude Code plugin bundles this server with 11 namespaced contributor commands and cross-platform reminder hooks:

/plugin marketplace add sceneview/claude-marketplace
/plugin install sceneview@sceneview

Claude Desktop

Settings → Developer → Edit Config, then add the standard mcpServers block above to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows). Restart after saving.

Cline

MCP Servers icon → Configure → Configure MCP Servers, or ~/.cline/mcp.json:

{ "mcpServers": { "sceneview": { "command": "npx", "args": ["-y", "sceneview-mcp"], "disabled": false, "autoApprove": [] } } }

Codex

codex mcp add sceneview -- npx -y sceneview-mcp

Or ~/.codex/config.toml — TOML, and the table is mcp_servers, not mcpServers:

[mcp_servers.sceneview]
command = "npx"
args = ["-y", "sceneview-mcp"]

The same config serves the Codex CLI, the IDE extension and the app.

Cursor

Add to .cursor/mcp.json (project) or ~/.cursor/mcp.json (global) — the standard mcpServers block above. Cursor also accepts an install link:

cursor://anysphere.cursor-deeplink/mcp/install?name=sceneview&config=eyJjb21tYW5kIjoibnB4IiwiYXJncyI6WyIteSIsInNjZW5ldmlldy1tY3AiXX0=

Gemini CLI

The repository root carries a gemini-extension.json, so the CLI installs the server straight from GitHub — no JSON to paste:

gemini extensions install https://github.com/sceneview/sceneview

This clones the whole monorepo — over 2 GB of history — because the CLI installs an extension by cloning the repository that declares it, and SceneView's manifest sits in a repository that also carries the Android, Apple, Web, Flutter and React Native sources. If you want the server and not the clone, paste the standard mcpServers block into ~/.gemini/settings.json instead; it is the same command, npx -y sceneview-mcp, and it costs a download of the npm package.

Either way, npx resolves sceneview-mcp to the latest version published on npm, which is not necessarily the version this manifest declares — the manifest's version describes the extension, and the server it launches updates on npm's cadence.

The extension declares nothing but the server: npx -y sceneview-mcp over stdio, no context file and no tool exclusions, so it adds the SceneView tools to a session and changes nothing else about it.

Gemini in Android Studio

Android Studio's MCP integration does not support stdio — it connects over HTTP only, so point it at the hosted endpoint. Settings → Tools → AI → MCP Servers:

{ "mcpServers": { "sceneview": { "httpUrl": "https://mcp.sceneview.dev/mcp", "enabled": true } } }

GitHub Copilot

In VS Code, .vscode/mcp.json — note the servers key, not mcpServers:

{ "servers": { "sceneview": { "type": "stdio", "command": "npx", "args": ["-y", "sceneview-mcp"] } } }

VS Code also accepts an install link:

vscode:mcp/install?%7B%22name%22%3A%22sceneview%22%2C%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22sceneview-mcp%22%5D%7D

In Copilot CLI:

copilot mcp add sceneview -- npx -y sceneview-mcp

JetBrains AI Assistant / Junie

Settings → Tools → AI Assistant → Model Context Protocol (MCP) → Add, then paste the standard mcpServers block above.

MCP Bundle (`.mcpb`)

mcp/manifest.json describes this server in the MCP Bundle format (spec 0.3), for desktop apps that install a local server from a bundle rather than a command line. It runs the built dist/index.js with the host's Node — the host provides the runtime and nothing else, so the bundle has to carry its own node_modules. Zipping this directory after npm run build alone produces a bundle that fails at launch with Cannot find package '@modelcontextprotocol/sdk':

npm ci                                # dev dependencies — the build needs them
npm run build                         # writes dist/
npm ci --omit=dev --ignore-scripts    # drop the dev tree, keep dist/
zip -r sceneview-mcp.mcpb manifest.json package.json dist node_modules

--ignore-scripts is not optional: this package's prepare script ends in tsc, and the same command that omits the dev dependencies omits TypeScript, so without it npm runs prepare, fails to find tsc and exits 127. Reinstall with a plain npm ci afterwards to get the dev tree back.

The manifest is not published as a .mcpb artefact by CI: it is the descriptor, and packing it stays a manual step for whoever needs a bundle. mcp/src/packaging.test.ts keeps its name, version, licence, entry point and Node range equal to package.json's — and does the same for server.json and the root gemini-extension.json — because nothing else would notice them drifting.

Use as a remote connector

No install, nothing to run: SceneView is hosted as a remote MCP server at

https://mcp.sceneview.dev/mcp

Any client that accepts a Streamable HTTP MCP URL can use it. In claude.ai, that is Settings → Connectors → Add custom connector.

Authless and read-only. There is no sign-in, no API key and no account: every tool is a pure function of the SDK's own documentation, samples and API surface, so there is nothing to authenticate and nothing of yours stored. All tools are annotated readOnlyHint except generate_3d_model, which calls an external generation service and is therefore marked open-world rather than read-only.

Prefer it local? npx -y sceneview-mcp runs the exact same server over stdio. The local route is the one that reads your project from disk (analyze_project) and the one that accepts your own SKETCHFAB_API_KEY / TRIPO_API_KEY; the hosted connector, being shared and anonymous, cannot.

Remote server (Streamable HTTP)

Some hosts cannot spawn a local process: they need MCP's Streamable HTTP transport at a public URL. The same package serves it:

npx sceneview-mcp --http
# [sceneview-mcp] v4.x — HTTP (remote tool surface)
# [sceneview-mcp] MCP endpoint: http://127.0.0.1:3333/mcp

Route	What it does
`POST /mcp`	MCP JSON-RPC (Streamable HTTP, stateless — no sessions, safe behind any load balancer)
`GET` / `DELETE /mcp`	`405` (no standalone SSE stream, no session to delete)
`GET /health`	`{"status":"ok","version":"4.x.y"}`
`GET /.well-known/openai-apps-challenge`	OpenAI domain verification — returns `OPENAI_APPS_CHALLENGE_TOKEN` as `text/plain`, `404` when unset
anything else	`404`

Configuration: PORT (default 3333), HOST (default 127.0.0.1 — set HOST=0.0.0.0 to expose it, and put HTTPS in front), OPENAI_APPS_CHALLENGE_TOKEN (the value OpenAI gives you when you submit the domain). CORS allows any origin. The usual SKETCHFAB_API_KEY / TRIPO_API_KEY / SCENEVIEW_TELEMETRY=0 knobs apply.

Everything is free, but not everything is remote. Three generation tools (render_3d_preview, create_3d_artifact, generate_scene) need your own third-party credentials, which a shared anonymous endpoint cannot hold, so the remote surface omits them and refuses those names at call time with a clear isError message pointing at the local npx sceneview-mcp path. stdio lists and runs all 32.

Inline 3D viewer. view_3d_model returns structuredContent plus _meta.ui.resourceUri = ui://widget/3d-viewer.html; the widget (SceneView.js + Filament.js, served by resources/read with the text/html;profile=mcp-app mime type and its _meta.ui.csp) renders the model inline in ChatGPT and any MCP Apps host.

Smoke test with curl (the Accept header is required by the spec):

curl -s http://127.0.0.1:3333/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-11-25","capabilities":{},"clientInfo":{"name":"curl","version":"0"}}}'
# {"jsonrpc":"2.0","id":1,"result":{"protocolVersion":"2025-11-25","capabilities":{"resources":{},"tools":{}},"serverInfo":{"name":"sceneview-mcp","version":"4.x.y"}}}

Point the ChatGPT connector / OpenAI mcp tool at https://<your-host>/mcp.

Already hosted. You do not have to run it yourself to get a public URL: the same code is deployed at https://mcp.sceneview.dev/mcp (GET /health answers {"status":"ok"}), which is what the remote connector above points at. Self-host when you want your own keys, your own rate limits, or analyze_project against a local checkout.

What you get

Every tool is free and there is no API key. The three generation tools that talk to a third-party service use your credentials; everything else works the moment the server starts.

Start here

Six tools carry most of what assistants actually do with SceneView. If you read nothing else:

Tool	What it does	Ask your assistant
`validate_code`	Compile-checks generated Kotlin or Swift against the real public API — symbol existence, 30+ rules, did-you-mean suggestions — before it reaches you	"Check that this SceneView code actually compiles"
`get_node_reference`	The exact signature, defaults and example for any of 48+ node types, instead of an invented one	"What are the parameters of ModelNode?"
`list_samples`	Browse 38 scenarios by tag (`ar`, `3d`, `ios`, `animation`, `geometry`, …)	"What SceneView samples involve AR planes?"
`get_sample`	Returns one of them complete and compilable, in Kotlin or Swift	"Give me the AR plane-placement sample in Kotlin"
`get_setup`	Gradle and manifest setup for Android 3D or AR	"Set up SceneView in my Android app"
`get_ar_setup`	Permissions, session options, plane detection, image tracking	"Add ARCore plane detection to this screen"

Full tool reference

Setup & integration

Tool	What it does
`get_setup`	Gradle + manifest setup for Android 3D or AR
`get_ios_setup`	SPM dependency, Info.plist, SwiftUI for iOS / macOS / visionOS
`get_web_setup`	Kotlin/JS + Filament.js (WASM) for browser-based 3D
`get_ar_setup`	Permissions, session options, plane detection, image tracking
`get_platform_setup`	Unified setup guide for any platform (Android, iOS, Web, Flutter, RN, Desktop, TV)

Code generation & migration

Tool	What it does
`get_sample`	Returns a complete, compilable code sample for any of 38 scenarios (Kotlin or Swift)
`list_samples`	Browse all samples, filter by tag (`ar`, `3d`, `ios`, `animation`, `geometry`, ...)
`validate_code`	Checks generated code against 30+ rules — including symbol existence against the real public API, with did-you-mean suggestions — before presenting it to the user
`migrate_code`	Automatically migrates SceneView 2.x / 3.x code with detailed changelog
`get_migration_guide`	Every breaking change with before/after code

API reference

Tool	What it does
`get_node_reference`	Full API reference for any of 48+ node types — exact signatures, defaults, examples
`list_platforms`	Supported platforms with their status, renderer, and framework
`get_platform_roadmap`	Multi-platform status and timeline

Guides

get_best_practices · get_animation_guide · get_gesture_guide · get_performance_tips · get_material_guide · get_collision_guide · get_model_optimization_guide · get_web_rendering_guide · get_troubleshooting · debug_issue

Discovery & analysis

Tool	What it does
`search_models`	Searches Sketchfab for free 3D models (BYOK — set `SKETCHFAB_API_KEY`)
`generate_3d_model`	Generates a brand-new GLB from a text prompt or image via Tripo AI (BYOK — set `TRIPO_API_KEY`)
`analyze_project`	Scans a local SceneView project on disk — detects platform, extracts version, flags outdated deps and known anti-patterns
`search_android_docs`	Searches Google's stock Android docs knowledge base (needs the `android` CLI on PATH)
`fetch_android_doc`	Fetches a full Android docs entry by its `kb://...` URI (needs the `android` CLI on PATH)

Inline 3D viewer (MCP Apps widget)

Tool	What it does
`view_3d_model`	Renders a public GLB / glTF URL in an interactive SceneView.js + Filament.js viewer inline in ChatGPT and any MCP Apps host (orbit, auto-rotate). For an https model it adds an "Open in AR on your phone" action (an `ar.sceneview.dev/open` link to the 3D AR Model Viewer app, with a QR code on desktop). Returns `structuredContent` + `_meta.ui.resourceUri`

5 resources

Resource URI	What it provides
`sceneview://api`	Complete SceneView 4.x API reference (the full `llms.txt`)
`sceneview://known-issues`	Live open issues from GitHub (cached 10 min)
`examples://demo-with-settings`	DemoScaffold v2 pattern — full-screen scene + Material 3 bottom sheet
`examples://sketchfab-streaming`	Streaming Sketchfab CC-BY models into a demo instead of bundling GLBs
`ui://widget/3d-viewer.html`	The 3D viewer widget (`text/html;profile=mcp-app`) that `view_3d_model` renders into

`search_models` — find real 3D assets from the AI

Generated SceneView code is only useful if it points at an asset that actually exists. search_models queries Sketchfab's public search API and returns a shortlist with names, authors, licenses, thumbnails, triangle counts, and viewer/embed URLs that the assistant can drop straight into rememberModelInstance(modelLoader, ...) or embed as a live preview.

Bring your own key (BYOK). SceneView never proxies the request — you keep the rate limit and the cost stays at zero. To set it up:

Create a free account at sketchfab.com/register
Copy your API token from sketchfab.com/settings/password
Set SKETCHFAB_API_KEY in your MCP client config:

{
  "mcpServers": {
    "sceneview": {
      "command": "npx",
      "args": ["-y", "sceneview-mcp"],
      "env": { "SKETCHFAB_API_KEY": "YOUR_TOKEN_HERE" }
    }
  }
}

Call it like search_models({ query: "red sports car", category: "cars-vehicles", maxResults: 6 }). If the key is missing, the tool returns a clear message explaining how to get one instead of failing silently.

`generate_3d_model` — create brand-new 3D assets from the AI

When no existing model fits, generate_3d_model closes the other half of the asset loop: it generates a fresh GLB from a text prompt (text→3D) or a source image (image→3D) via the Tripo AI API, then returns a direct GLB download URL plus license/attribution metadata — ready for rememberModelInstance(modelLoader, ...) and AR placement.

Two quality tiers:

`quality`	Tripo model	Topology	Latency	Approx. cost (July 2026)
`"fast"` (default)	P1 (`P1-20260311`)	low-poly, AR-ready	~25–30 s	~$0.10–0.25 of your credits
`"hd"`	H3.1 (`v3.1-20260211`)	quad mesh, detailed geometry + textures	up to ~100 s	~$0.41 of your credits

Bring your own key (BYOK). Exactly like search_models: SceneView never proxies the request or holds your key — generations are billed to your Tripo account. To set it up:

Create an API key at platform.tripo3d.ai/api-keys (new accounts get free trial credits)
Set TRIPO_API_KEY in your MCP client config:

{
  "mcpServers": {
    "sceneview": {
      "command": "npx",
      "args": ["-y", "sceneview-mcp"],
      "env": { "TRIPO_API_KEY": "YOUR_KEY_HERE" }
    }
  }
}

Call it like generate_3d_model({ prompt: "a low-poly cactus in a striped pot" }) or generate_3d_model({ imageUrl: "https://example.com/chair.jpg", quality: "hd" }). Provide exactly one of prompt / imageUrl.

⚠️ The GLB download URL expires ~5 minutes after generation — download the file immediately and self-host it (e.g. copy it into your app's assets/models/). The tool result repeats this warning. Missing key, task failures, rate limits, and poll timeouts (2 min fast / 4 min hd cap) all return clear, actionable messages instead of hanging or crashing.

`analyze_project` — local project scan

Because the MCP server runs on the user's machine, analyze_project can read their project files directly. Given a path (default: process.cwd()), it:

Detects the project type by looking for build.gradle(.kts) with io.github.sceneview:sceneview (Android), Package.swift with SceneViewSwift (iOS), or package.json with sceneview-web (Web).
Extracts the SceneView dependency version and compares it against the latest release known to this MCP build, flagging outdated projects.
Walks up to 30 source files (.kt, .kts, .swift, .js, .ts) and up to 500 KB total, scanning for well-known anti-patterns: Filament/ModelLoader calls inside background coroutines, the LightNode(...) { ... } trailing-lambda bug, deprecated 2.x APIs (ArSceneView, TransformableNode, PlacementNode, ViewRenderable, loadModelAsync), and com.google.ar.sceneform.* imports.
Returns a structured { projectType, sceneViewVersion, latestVersion, isOutdated, warnings, suggestions } report, plus a Markdown summary.

The tool is read-only, never writes to disk, and gracefully handles missing directories. Use it when the user asks "is my project up to date?" or as a quick sanity check before generating new code for an existing codebase.

Examples

"Build me an AR app"

The assistant calls get_ar_setup + get_sample("ar-model-viewer") and returns a complete, compilable Kotlin composable with all imports, Gradle dependencies, and manifest entries. Ready to paste into Android Studio.

"Create a 3D model viewer for iOS"

The assistant calls get_ios_setup("3d") + get_sample("ios-model-viewer") and returns Swift code with the SPM dependency, Info.plist entries, and a working SwiftUI view.

"What parameters does LightNode accept?"

The assistant calls get_node_reference("LightNode") and returns the exact function signature, parameter types, defaults, and a usage example — including the critical detail that apply is a named parameter, not a trailing lambda.

"Validate this code before I use it"

The assistant calls validate_code with the generated snippet and checks it against 30+ rules: symbol existence against the real public API (unknown imports, made-up node types, nonexistent loader methods — each with did-you-mean suggestions), threading violations, null safety, API correctness, lifecycle issues, deprecated APIs. Problems are flagged with explanations before the code reaches the user.

Why this exists

Without this MCP server, AI assistants regularly:

Recommend deprecated Sceneform (abandoned 2021) instead of SceneView
Generate imperative View-based code instead of Jetpack Compose
Use wrong API signatures or outdated parameter names
Miss the LightNode named-parameter gotcha (apply = not trailing lambda)
Forget null-checks on rememberModelInstance (it returns null while loading)
Have no knowledge of SceneView's iOS/Swift API at all

With this MCP server, AI assistants:

Always use the current SceneView 4.x API surface
Generate correct Compose-native 3D/AR code for Android
Generate correct SwiftUI-native code for iOS/macOS/visionOS
Know about all 48+ node types and their exact parameters
Validate code against 30+ rules before presenting it
Provide working, tested sample code for 38 scenarios

Quality

The MCP server is tested with 2,015 unit tests across 91 test files covering:

Every tool response (correct output, error handling, edge cases)
All 38 code samples (compilable structure, correct imports, no deprecated APIs)
Code validator rules (true positives and false-positive resistance)
Node reference parsing (all node types extracted correctly from llms.txt)
Resource responses (API reference, GitHub issues integration, the 3D viewer widget)
The Streamable HTTP surface end to end (initialize, free-only tools/list, widget resource, health, OpenAI challenge)

 Test Files  91 passed (91)
      Tests  2015 passed (2015)

All tools work fully offline except sceneview://known-issues (GitHub API, cached 10 min), search_models (Sketchfab, BYOK), and generate_3d_model (Tripo AI, BYOK). Anonymous telemetry also makes a network call unless SCENEVIEW_TELEMETRY=0 (see below).

Troubleshooting

"MCP server not found" or connection errors

Ensure Node.js 18+ is installed: node --version
Test manually: npx sceneview-mcp — should start without errors
Restart your AI client after changing the MCP configuration

"npx command not found"

Install Node.js from nodejs.org (LTS recommended). npm and npx are included.

Server starts but tools are not available

Claude Desktop: check the MCP icon in the input bar — it should show "sceneview" as connected
Cursor: check Settings > MCP for green status
Restart the AI client to force a reconnect

Firewall or proxy issues

The only network calls are to the GitHub API (for known issues), Sketchfab (when SKETCHFAB_API_KEY is set), Tripo AI (when TRIPO_API_KEY is set and generate_3d_model is called), and the anonymous telemetry endpoint (off with SCENEVIEW_TELEMETRY=0). Everything else works offline.

{
  "mcpServers": {
    "sceneview": {
      "command": "npx",
      "args": ["-y", "sceneview-mcp"],
      "env": {
        "HTTPS_PROXY": "http://proxy.example.com:8080"
      }
    }
  }
}

Sponsor

If sceneview-mcp saves you time, consider donating on Open Collective, or GitHub Sponsors if you prefer. Building this is a one-dev labor of love; every tool is free with or without a donation.

Anonymous telemetry

Enabled by default (MCP client name/version and tool names — no personal data, no prompt content). Opt out with SCENEVIEW_TELEMETRY=0. See PRIVACY.md for the full payload shape.

Development

cd mcp
npm install
npm run prepare  # Copy llms.txt + build TypeScript
npm test         # vitest suite
npm run lint     # Biome (repo-root biome.json) — lint + format + import assists
npm run lint:fix # same, applying the safe fixes
npm run dev      # Start with tsx (hot reload)

Project structure

mcp/
  src/
    index.ts             # CLI entry point — stdio, or Streamable HTTP with --http
    server.ts            # The MCP Server (resources + tools), shared by both transports
    http.ts              # Streamable HTTP entrypoint (/mcp, /health, OpenAI challenge)
    widgets.ts           # MCP Apps widget: ui://widget/3d-viewer.html (SceneView.js + Filament.js)
    tools/handler.ts     # Tool dispatcher
    surfaces.ts          # Which tools the anonymous remote surface serves
    samples.ts           # 38 compilable code samples (Kotlin + Swift)
    validator.ts         # Code validator (30+ rules)
    node-reference.ts    # Node type parser
    guides.ts            # Best practices, AR setup, roadmap, troubleshooting
    migration.ts         # v2 -> v3 -> v4 migration guide
    preview.ts           # 3D preview URL generator
    artifact.ts          # HTML artifact generator (model-viewer, charts, product 360)
    issues.ts            # GitHub issues fetcher (cached)
    search-models.ts     # Sketchfab BYOK search
    generate-model.ts    # Tripo BYOK text/image -> GLB generation
    analyze-project.ts   # Local project scanner
  llms.txt               # Bundled API reference (copied from repo root)

Contributing

Fork the repository
Create a feature branch
Add tests for new tools or rules
Run npm test — all tests must pass
Submit a pull request

See CONTRIBUTING.md for the full guide.

Legal

LICENSE — MIT License
TERMS.md — Terms of Service
PRIVACY.md — Privacy Policy