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flutter-lamp
Advanced tools
Flutter Lamp — an MCP server giving AI live eyes on a running Flutter app over the Dart VM Service: exceptions, logs, network, frames, memory + root-cause diagnosis.
An MCP server that connects Claude Code, Claude Desktop, Cursor, Codex & Gemini directly to a running Flutter app through the Dart VM Service Protocol — streaming exceptions, logs, network calls, frame timings and memory as structured data, plus an evidence-first root-cause diagnosis engine and a live browser dashboard.
Today you debug Flutter with your AI by copy-pasting stack traces, flutter run
output and DevTools screenshots. The AI is blind between messages.
Flutter Lamp makes the AI runtime-aware. It reads the app's live state over official Flutter/Dart APIs (never scraping DevTools), so instead of "paste the error" the AI can ask the app "what just happened, and why?"
❌ You: *pastes 40 lines of red stack trace*
✅ AI: connect_vm → get_exceptions → diagnose_runtime
→ "RenderFlex overflow in Column at home.dart:42, triggered right after
GET /api/user returned 500. Confidence 85%. Fix: …"
dart:developer)dart:io profiling — covers Dio & package:http, no interceptor neededdiagnose_runtime — correlates evidence into summary · root cause · evidence · confidence · fixes; says "Unknown" below 70% instead of hallucinatinghttp://127.0.0.1:7373 — streams everything over WebSocket, independent of the AI connectionflutter-runtime-diagnosis Claude Code skill| Tool | Purpose | Safety |
|---|---|---|
connect_vm | Connect to a running app's Dart VM Service (ws:// or http://). | mutates |
ensure_tcp_device | Android transports; recommend wireless, optionally promote USB. | mutates |
runtime_health | One-call triage — verdict plus exception/network/frame/log/memory summary. | read-only |
what_changed | Evidence from the window before a failure, with a timeline. | read-only |
get_navigation | Current route and recent transitions, with per-route failures. | read-only |
get_rebuilds | Widget rebuild hotspots resolved to widget, file and line. | read-only |
get_state_activity | Riverpod provider activity over time, and how often it coincides with build-heavy frames. | read-only |
export_session | The whole session as versioned JSON — brief (diagnoses + cited evidence) or full. | read-only |
explain_diagnosis | Why a diagnosis was reached: resolved evidence, alternatives, gaps. | read-only |
get_capabilities | Active collectors, tool safety classes, what cannot be observed. | read-only |
runtime_status | Connection, session, reconnect state, event counts, retention window. | read-only |
get_logs | Console + dart:developer logs (filter by severity / source / text). | read-only |
get_exceptions | Framework & unhandled exceptions with stack traces. | read-only |
get_frames | Frame build/raster timings; onlyJanky filter. | read-only |
get_network | HTTP requests (Dio & package:http); headers + timing on failures. | read-only |
get_widget_tree | Widget-tree snapshot from the Flutter Inspector. | read-only |
get_selected_widget | Widget currently selected in the Inspector. | read-only |
get_memory | Dart heap + external memory (MB). | read-only |
get_timeline | Recent VM timeline events (build/paint/layout/GC). | mutates |
diagnose_runtime | Root cause with evidence ids, timeline, alternatives, limitations. | read-only |
diagnose_performance | Why the app is janky — percentiles, phase split, rebuild attribution. | read-only |
get_dashboard_url | URL of the live browser dashboard. | read-only |
mutates means the tool changes app or VM state, not your project — nothing
here writes code or files. connect_vm enables dart:io HTTP timeline logging
on the app so network capture works; get_timeline with recordFrom: true
changes the VM's recording flags. Every tool carries an MCP readOnlyHint
annotation so a client can enforce this itself.
Agents should not call all sixteen. The recommended flow is
runtime_health → what_changed → a targeted get_* → diagnose_runtime;
see AI Agent Integration.
Requires Node ≥ 20. Nothing to clone — npx fetches it on first run:
npx -y flutter-lamp
Or install it globally:
npm install -g flutter-lamp
git clone https://github.com/itsonu/flutter-lamp.git
cd flutter-lamp
npm install
npm run build
node dist/index.js
Add the server to your MCP client config, then restart the client.
Claude Code — .mcp.json in your project:
{
"mcpServers": {
"flutter-lamp": {
"command": "npx",
"args": ["-y", "flutter-lamp"]
}
}
}
Or from the CLI:
claude mcp add flutter-lamp -- npx -y flutter-lamp
Cursor (~/.cursor/mcp.json) and Claude Desktop
(claude_desktop_config.json) use the same mcpServers shape.
Running from a clone instead? Point command at node and args at
/absolute/path/to/flutter-lamp/dist/index.js.
Optional environment variables:
| Var | Default | Meaning |
|---|---|---|
DASHBOARD_PORT | 7373 | Dashboard HTTP/WS port. |
DASHBOARD_HOST | 127.0.0.1 | Bind address (localhost only by default). |
DASHBOARD_DISABLE | — | Set to 1 to disable the dashboard. |
FLUTTER_LAMP_REDACT | on | Set to off to keep raw credential values. |
FLUTTER_LAMP_REDACT_EXTRA | — | Comma-separated extra header-name patterns to redact. |
Run your Flutter app in debug/profile mode:
flutter run
Copy the line it prints:
A Dart VM Service on <device> is available at: http://127.0.0.1:PORT/TOKEN=/
Tip:
flutter run --vm-service-port=8181gives a stable URI across restarts.
Ask your AI to connect and diagnose — e.g. "connect to my Flutter app at
<uri> and tell me why it's throwing." With the bundled skill, Claude Code
runs the whole flow (connect → gather → diagnose_runtime) automatically and
never asks you to paste logs when the VM Service is reachable.
Open http://127.0.0.1:7373 in a browser for the live dashboard — it runs alongside the AI, not instead of it.
A flutter run started over USB loses its VM Service tunnel the moment the
cable moves; one started over a TCP transport does not. Ask the server which
transports exist:
ensure_tcp_device # read-only: lists transports, recommends one
ensure_tcp_device { promote: true } # puts a USB-only device on TCP (needs the cable once)
Then launch against the wireless serial it recommends:
flutter run -d 192.168.88.3:5555
The cable is only needed for the one-time promotion. Reverse it any time with
adb usb.
A zero-dependency dark UI (native HTTP + WebSocket, no build step) that streams runtime data as it happens:
Overview (connection · FPS · memory · event counts) · Logs (search / filter / auto-scroll) · Network (expandable headers & timing) · Exceptions (expandable stack traces) · Timeline · Performance (live canvas charts) · Inspector.
Controls: pause/resume · clear view · export JSON · per-tab search · auto-reconnect.
Running Flutter app
│ Dart VM Service Protocol (JSON-RPC over WebSocket)
▼
VmService client ──▶ Collectors (log · exception · frame · network · …)
│
▼
RuntimeStore (one centralized, capped event stream;
every event: timestamp · source · severity · category)
│ │
┌──────────┘ └──────────┐
▼ ▼
MCP tools (stdio) Dashboard (HTTP + WebSocket)
→ Claude Code / Cursor / … → your browser
Everything flows through one event store. Adding a new runtime source =
implement the Collector interface and register it — no other layer changes.
Design principles: official Flutter/Dart APIs only, structured JSON over text,
never scrape DevTools, and never claim a cause the evidence doesn't support.
dart:io HTTP
profiling are not available in release builds.dart:io HTTP,
so requests are fetched when get_network or diagnose_runtime runs — not
streamed continuously.Debug.PauseException needs pause-on-exception enabled in the app to fire.
Framework errors (Flutter.Error) are always captured regardless.get_network.runtime_status reports what is
retained, what was evicted, and the oldest event still held.127.0.0.1 by default. Change DASHBOARD_HOST only on a
network you trust — runtime data is served unauthenticated.Runtime data is sensitive. HTTP headers carry bearer tokens and cookies, URIs carry API keys, and developers print credentials into logs — and everything captured is handed to an AI model and streamed to any browser watching the dashboard.
Secrets are redacted at capture, so they never enter the event store and no
consumer can leak what was never stored. Redacted by default: Authorization,
Proxy-Authorization, Cookie, Set-Cookie, WWW-Authenticate, any header
whose name contains token, secret, password, credential, api-key or
session, sensitive query-string parameters, and JWT- or Bearer-shaped
strings in log lines and error text. Header names that were hit are listed in
data.redactedHeaders so you can see that something was withheld rather than
getting a silently partial picture. Add patterns with
FLUTTER_LAMP_REDACT_EXTRA, or disable entirely with FLUTTER_LAMP_REDACT=off
for a local-only session.
The dashboard is not exposed to other pages. Binding to loopback does not
protect a WebSocket — browsers exempt WebSocket from the same-origin policy, so
without a check any page you have open could connect to
ws://127.0.0.1:7373/ws and read your whole runtime stream. The handshake
requires a per-process token that is inlined into the served page, which
cross-origin script cannot read, and a present Origin header must be loopback.
The page is served X-Frame-Options: DENY, and /health returns liveness only
— never the VM Service URI, which embeds the VM's own auth token.
Setting DASHBOARD_HOST to a non-loopback address logs a warning and puts
runtime evidence on your network. Only do that on a network you trust.
Found a vulnerability? See SECURITY.md.
Shipped: VM connect, logs, exceptions (with stacks), network, frames, widget tree, memory, timeline, diagnosis, live dashboard.
Next: deeper correlation (memory/timeline into diagnose_runtime), CPU
sampling & leak heuristics, Riverpod/Bloc state, navigation, knowledge graph,
auto-fixes. See docs/Phases.md.
npm run build # compile TypeScript → dist/
npm run watch # incremental compile
npm test # node:test suite (engine + collectors + dashboard)
npm test runs the compiled output, so build first. It relies on glob support in
node --test, which needs Node ≥ 21 — the server itself runs on Node ≥ 20.
Docs live in docs/:
| Doc | Contents |
|---|---|
| PRD | Problem, users, principles, non-goals, constraints |
| Architecture | Data flow, components, event model, how to add a collector |
| Rules | Non-negotiable constraints every change is checked against |
| Phases | Roadmap and status |
| AI Agent Integration | The investigation protocol agents should follow |
| Improvement Plan | Current audit and prioritized backlog |
| Observability Roadmap | Architecture target, phases, and explicit rejections |
| Releasing | How versions are staged, tagged and published |
| Design | Dashboard visual and interaction spec |
| Implementation Notes | Non-obvious things learned building it |
Issues and PRs welcome. Keep it modular, prefer official APIs over hacks, and
add a node:test check for any non-trivial logic.
MIT © Chandrabhushan Prakash
FAQs
Flutter Lamp — an MCP server giving AI live eyes on a running Flutter app over the Dart VM Service: exceptions, logs, network, frames, memory + root-cause diagnosis.
The npm package flutter-lamp receives a total of 254 weekly downloads. As such, flutter-lamp popularity was classified as not popular.
We found that flutter-lamp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.

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