Ghidra MCP Zig

The Ghidra MCP Zig Plugin solves a common bottleneck in reverse‑engineering workflows: the need to expose Ghidra’s rich analysis capabilities through a lightweight, type‑safe interface that can be consumed by modern AI assistants. By running a Zig‑based MCP server alongside the Ghidra plugin, developers can have an AI model query program structure, request decompiled snippets, or manipulate symbols in real time without leaving the IDE. This integration turns Ghidra from a standalone reverse‑engineering tool into an interactive knowledge base that an AI can interrogate, greatly accelerating analysis cycles and enabling novel automation patterns.

At its core, the server implements the Model Context Protocol (MCP) over a local socket. It exposes three primary services: function decompilation, symbol management, and an import/export listing API. When the AI client sends a request, the server forwards it through a JNI bridge written in Zig to Ghidra’s Java API, retrieves the requested data, and streams it back in a JSON‑compatible format. The use of Zig for the bridge brings deterministic memory handling and zero‑cost abstractions, reducing runtime overhead compared to traditional Java or C++ wrappers. The plugin also includes a Go client library that abstracts the MCP protocol, allowing developers to embed AI calls directly into custom scripts or CI pipelines.

Key features that distinguish this MCP server include:

• JNI‑based communication: A type‑safe bridge written in Zig ensures safe, efficient calls into Ghidra’s Java runtime.
• Function decompilation and renaming: AI assistants can request human‑readable C code for any function, then rename it based on context or naming conventions.
• Data symbol management: The server can list, create, and modify symbols, enabling AI‑driven refactoring or automated naming schemes.
• Import/Export listing: Comprehensive views of external references help the AI reason about dependencies and potential vulnerabilities.
• Modern build ecosystem: Zig for server logic, Gradle for the Java plugin, and Go for client tooling provide a cohesive development experience across languages.

Real‑world scenarios that benefit from this plugin include:

• Dynamic code analysis: An AI assistant can ask “Show me the decompiled body of ” while a researcher is inspecting a binary, instantly receiving the output without manual navigation.
• Automated naming conventions: By sending a list of functions to the AI, it can suggest meaningful names based on heuristics or contextual data, which the server then applies via Ghidra’s symbol API.
• Continuous integration security checks: CI pipelines can query the MCP server to verify that no new imports have been added to a binary after a certain commit, flagging potential backdoors automatically.
• Education and training: Students can interact with the MCP server through a conversational interface, asking questions about binary structure and receiving immediate, programmatic answers.

Integration into existing AI workflows is straightforward. The Go client library can be dropped into any application that already communicates with an MCP server, while the Zig server runs as a background process. Because the protocol is stateless and uses standard JSON payloads, any AI model that supports HTTP or WebSocket can become a first‑class citizen in the reverse‑engineering pipeline. This tight coupling between Ghidra and AI tools transforms static analysis into an interactive, context‑aware experience that scales with the complexity of modern binaries.