⚡ Fully automated MCP server + JADX plugin built to communicate with LLM through MCP to analyze Android APKs using LLMs like Claude — uncover
⚡ Fully automated MCP server + JADX plugin built to communicate with LLM through MCP to analyze Android APKs using LLMs like Claude — uncover vulnerabilities, analyze APK, and reverse engineer effortlessly. That is what the jadx ai mcp mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
The server publishes 1 tool. What each one is for:
get_manifest_component — Retrieve specific manifest component instead of whole manifest fileThe server ships on PyPI as httpx, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Jadx Ai MCP's toolset — get_manifest_component — is a fair guide to whether it matches your workflow. It is maintained by zinja-coder; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Jadx Ai MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| get_manifest_component | Retrieve specific manifest component instead of whole manifest file |
{
"mcpServers": {
"jadx-ai": {
"command": "uvx",
"args": ["httpx"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.