"A framework for building cross-platform mobile applications using Flutter" matching MCP servers:

An MCP server that wraps Flutter CLI tools into structured, agent-friendly tools for AI coding agents, with features like two-phase test results, output capping, and managed app lifecycle.

Provides Flutter best practices, code analysis, and state management recommendations for senior developers.

Enables interaction with the Flutter SDK by providing tools to analyze and apply fixes to Dart and Flutter files.

Enables Android APK reverse engineering and Flutter runtime injection through a six-step pipeline of decompile, analyze, synthesize, inject, patch, and repackage. Provides MCP tools for authorized security research and penetration testing.

Enables cross-validation of Claude's plans and code reviews using GPT-5.2, providing independent assessment with structured JSON output.

Lets coding agents statically scan Swift, Kotlin/Java, Dart, React Native and manifest files for crash-prone operators, main-thread deadlocks, missing accessibility labels, insecure token storage, cleartext traffic and vague permission strings, and cross-check platform APIs against Info.plist, PrivacyInfo.xcprivacy and AndroidManifest permission/exported settings. It also runs syntax or full build verification, returning FAIL on error-level findings so agents must fix issues before claiming work is done.

Remote MCP server that gives coding agents verified, version-pinned answers about fast-moving frameworks, starting with Expo SDK 54.

Enables AI coding agents to extract Figma design data and convert it into Flutter widgets and screens. Supports theme setup, component analysis, asset exports, and provides Flutter implementation guidance from Figma designs.

Enables AI agents to dynamically instrument Android and iOS apps through Frida 17, exposing 40 tools for exploring apps, hooking Java/ObjC/native methods, tracing calls, bypassing SSL pinning, capturing TLS keys and plaintext, and inspecting memory. Results use Ghidra/IDA-style module+offset addresses and carry an evidence envelope, with optional UI automation through mobile-mcp.

An MCP server that enables AI assistants to analyze Android APK and iOS IPA files for security issues through natural language conversation, including permission auditing, secret detection, and SDK enumeration.

Analyzes codebases for cross-platform compatibility issues, detecting platform-specific APIs, file path problems, line ending inconsistencies, and shell command incompatibilities across Windows, macOS, and Linux.

Enables AI agents to explore, search, and reference code implementation details across different project repositories. It also supports a task delegation protocol for agents to request and track work between separate codebases.

Contract-driven test enforcement and reporting for LLM-generated code via MCP, VS Code, Copilot CLI, Claude, Cursor, or Python SDK.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables MCP clients to drive the complete mobile security toolkit—static/dynamic analysis, Frida hooks, runtime exploration, traffic interception, network setup, and IoT scans—through engine-exposed tools.

Provides advanced Flutter development tools via Model Context Protocol with features including widget analysis, documentation search, performance optimization, and code generation.

Enables authorized Android security testing with static and dynamic analysis, Frida instrumentation, storage inspection, and traffic interception via MCP tools.

Local-first MCP server providing deterministic Flutter/Dart static analysis, knowledge search, and project review with structured insights and confidence-based findings over an indexed SQLite store of official repositories.