JADX AI MCP

At a glance

Best for

Reverse engineering Android apps and auditing them for security issues.

Inference engine

• uses your MCP client’s model server

GPU support

None of its own: it talks to a llama.cpp or MLX LM server

Opens

• GGUF
• MLX

Features

• MCP tools
• Tool calling

Platforms

Runs inside JADX (free) with Java 11+; the MCP server needs Python 3.10+.

Install

jadx plugins --install "github:zinja-coder:jadx-ai-mcp"

jadx plugins --install "github:zinja-coder:jadx-ai-mcp"

jadx plugins --install "github:zinja-coder:jadx-ai-mcp"

Then connect an MCP client that runs a local model, such as LM Studio, Jan or OpenCode, to its server, and serve a model that supports tool calling. Its README has a setup video for LM Studio.

5 models on this site run in JADX AI MCP

Models tagged for reverse engineering that support tool calling, with a GGUF or MLX build to serve locally. Raw decompilers such as LLM4Decompile run on their own in llama.cpp or LM Studio instead.

Model	Made by	Type	Size	Opens it as	Quality tier
ZAGLM-4.7 Flash (30B-A3B)	Zhipu AI	LLM	31B (3.0B active)	GGUF or MLX	88
Devstral 2 (123B)	Mistral AI	LLM	125B	GGUF or MLX	86
Qwen3 Coder 30B-A3B	Alibaba	LLM	31B (3.3B active)	GGUF or MLX	78
Qwen3 30B-A3B	Alibaba	LLM	31B (3.3B active)	GGUF or MLX	74
OAgpt-oss 20B	OpenAI	LLM	21B (3.6B active)	GGUF or MLX	70

Alternatives

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Which app should I use?