Flutter Inspector MCP Server
The Flutter Inspector MCP Server enables AI-powered development by connecting Flutter applications to AI coding assistants via the Model Context Protocol. It provides comprehensive tools for inspection, debugging, and performance monitoring:
Error Analysis: Retrieve condensed error information with
get_app_errorsDevelopment Tools: Perform hot reloads and capture screenshots
Widget Tree Inspection: Get details about layout, parent chains, children summaries, and selected widgets
Debugging Capabilities: Dump render, layer, semantics, and focus trees; toggle visual aids like baseline paint, layers, and repaint rainbow
Performance Monitoring: Track widget rebuilds, repaints, and profile socket/HTTP operations
Environment Control: Override platform, brightness, and time dilation for testing
Utility Methods: Get VM info, supported protocols, active ports, and discover available extension RPCs
Integration: Works with AI tools and is registered with Smithery for discoverability
Interfaces with Dart VM Service Protocol to access Flutter/Dart process information, retrieve version details, and monitor various event streams for debugging Flutter applications.
Connects to a Flutter app's debug mode to extract data about widget trees, render trees, layer trees, and semantics, enabling AI tools to provide context-aware assistance for Flutter app development.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Flutter Inspector MCP Servercheck for layout issues in my current screen"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
flutter-mcp-toolkit
Inspect and drive a running Flutter app from your AI assistant.
π Docs: docs.page/arenukvern/mcp_flutter
π€ Contribute: guide Β· contributors Β· code of conduct Β· security
flutter-mcp-toolkit is a Dart MCP server + Flutter package that lets AI Agents (Codex, Zed, Cursor, Intent, Claude Code, Cline, etc..) take (semantic snapshots, tap widgets, type into forms, hot-reload, and read logs from a Flutter app) or create its own tools and resources at runtime using MCP Toolkit β without leaving the conversation and work with Flutter apps in closed feedback loop - see example of it described in OpenAI Agentic Harness.

The picture's story: the toolkit gives an AI assistant a shared window and control loop into a running Flutter app, so it can inspect state, act like a user, hot reload, read proof, and use custom tools from your app instead of guessing.

![NOTICE]: Version 4 is currently a prerelease train. Use
4.0.0-dev.5only if you are intentionally testing the new architecture; otherwise stay on the latest stable 3.x release until4.0.0is promoted.
Get started in 4 steps
# 1. Install the binary
curl -fsSL https://raw.githubusercontent.com/Arenukvern/mcp_flutter/main/install.sh | bash
# Installs flutter-mcp-toolkit plus the short fmtk alias for repeated CLI loops.
# 2. Add the toolkit to your Flutter app
cd my-flutter-app
flutter-mcp-toolkit codegen-init # adds mcp_toolkit + emits main.dart snippet
# 3. Install skills for your AI agent
flutter-mcp-toolkit init claude-code # or: cursor | codex | cline | agents-skills | all
# Alternative (skills only): npx skills add Arenukvern/mcp_flutter -a cursor -y
# 4. Run
flutter run --debugThat's it. Your AI agent can now inspect and drive the running app β and your app can expose custom MCP tools at runtime (see Dynamic Tools Registration below).
Related MCP server: Sketch Context MCP
π° News
2026-05-26 β v3.1.0: Platform-view capture routing, macOS/iOS Simulator host screenshots, web CDP tab capture (SCK β CDP β flutter_layer), and cross-platform showcase platform views.
Install from marketplaces
Platform | Command / link |
Any agent (recommended) |
|
Claude Code (git catalog) |
|
Codex (git catalog) |
|
Cursor (local plugin) |
|
Skills only |
|
MCP registries |
Maintainers submitting to official stores: marketplace submission runbook. Full matrix: marketplace distribution.
Documentation
Docs for AI Agent and Human - wiki + llms.txt
Migrating v2 β v3 β
fmt_*MCP tools, binaries, client config keys,validate-runtime.MCPCallEntry to AgentCallEntry migration β
MCPCallEntryremoval,AgentCallEntry, platformcodegen sync,fmt_migrate_agent_entries.IntentCall consumer guide β hosted
intentcall_*dependency policy, consumer proof gates, and the boundary betweenmcp_flutterand upstream IntentCall architecture.Why this repo matters β what it is, why it exists.
CLI vs MCP β pick the right mode.
Feature map β the 30 tools.
AI agent setup - for AI Agents.
Marketplace distribution β Claude, Cursor, Codex, skills.sh.
Architecture β for contributors.
Published packages
Package | Pub.dev | Role |
|
| Flutter app package for runtime MCP tools/resources and toolkit bootstrap. |
|
| Pure-Dart shared command/result/capability types. |
|
| Capability kernel contracts for composable MCP units. |
|
| Server-side |
The server binary lives in mcp_server_dart and is shipped through GitHub
Release artifacts as flutter-mcp-toolkit, fmtk, and
flutter-mcp-toolkit-server; it is not a pub.dev package.
Development support
Need | Start here |
Contribute code or docs | |
Add or credit contributors | |
Report vulnerabilities | |
Validate local changes |
|
Maintain releases | |
Install or update agent skills |
What it does
The default toolkit surface exposes 30 MCP tools under the fmt_* capability prefix across four categories:
Inspection β semantic snapshot, view details, errors, screenshots, VM info
Interaction β tap, scroll, type, fill forms, hot-reload, navigate, wait_for
Debug β recent logs, evaluate Dart expressions
Lifecycle β discover apps, hot-reload, hot-restart
See the flutter-mcp-toolkit-{guide,inspect,control,debug} skills for the full
reference (installed by flutter-mcp-toolkit init or npx skills add Arenukvern/mcp_flutter).
Install options: AI agent setup.
Dynamic Tools Registration
Flutter apps can register custom tools and resources at runtime. See how it
works in this short YouTube video.
The same arguments.connection targeting is supported by the CLI's exec,
batch, daemon command/execute, daemon watch/start, and snapshot step args.
There is officialMCP Server for Flutter from Flutter team which exposes Dart tooling. The main goal of this project is to bring power of MCP server tools by creating them in Flutter app, using dynamic MCP tools registration and close feedback loop for AI Agent. See how it works in short YouTube video. See Quick Start for more details. See original motivation behind the idea.
β οΈ Note on Dump RPCs
Dump RPC methods (like dump_render_tree) can produce huge token output and
are disabled by default. Enable with --dumps. See
mcp_server_dart README for the full flag surface.
π Security
Generally, since you use MCP server to connect to Flutter app in Debug Mode, it should be safe to use. However, I still recommend to review how it works in ARCHITECTURE.md, how it can be modified to improve security if needed.
This MCP server is verified by MseeP.ai.
π§ Troubleshooting
Connection Issues
Ensure your Flutter app is running in debug mode
Verify the port matches in both Flutter app and MCP server
Check if the port is not being used by another process
Safest explicit targeting: use
arguments.connection.uriand paste exact Flutter machineapp.debugPort.wsUriIf response includes
connection_selection_required, retry witharguments.connection.targetIdusing one URI fromavailableTargets(or setarguments.connection.uridirectly)
AI Tool Not Detecting Inspector
Restart the AI tool after configuration changes
Verify the configuration JSON syntax
Check the tool's logs for connection errors
Dynamic Tools Not Appearing
Ensure
mcp_toolkitpackage is properly initialized in your Flutter appCheck that tools are registered using
MCPToolkitBinding.instance.addEntries()Use
fmt_list_client_tools_and_resourcesto verify registrationHot reload your Flutter app after adding new tools
The Flutter MCP Server is registered with Smithery's registry, making it discoverable and usable by other AI tools through a standardized interface.
Integration Architecture
βββββββββββββββββββ βββββββββββββββββββββββββ βββββββββββββββββββ
β β β Flutter App with β β β
β Flutter App β<--->β mcp_toolkit β<--->β flutter-mcp- β
β (Debug Mode) β β (VM Svc. Extensions β β toolkit-server β
β β β + Dynamic Tools) β β β
βββββββββββββββββββ βββββββββββββββββββββββββ βββββββββββββββββββπ€ Contributing
Contributions are welcome! See CONTRIBUTING.md (maintainer releases, binary checksums) and the contribution guide. Pull requests and issues: GitHub.
β¨ Contributors
Huge thanks to all contributors for making this project better!
This roster is maintained with all-contributors.
To add someone, update .all-contributorsrc and
regenerate the README table, or use the all-contributors bot/CLI from a PR.
More detail: docs/contributing/contributors.mdx.
π Learn More
Star History
π License
MIT - Feel free to use in your projects!
Flutter and Dart are trademarks of Google LLC.
Maintenance
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