GhidraMCP Overview

GhidraMCP is a Model Context Protocol (MCP) server that turns the Ghidra reverse‑engineering platform into an AI‑friendly workspace. By exposing Ghidra’s rich data model—functions, decompiled code, imports/exports, memory maps, and more—through a lightweight socket interface, the server allows AI assistants such as Claude to interrogate binaries with natural language. This eliminates the need for manual navigation, enabling analysts to ask questions like “Which functions call ?” or “Show me the decompiled code for the function at 0x401000” and receive precise, context‑aware answers instantly.

The server solves a common bottleneck in binary analysis: the steep learning curve and repetitive manual steps required to surface useful information from a complex codebase. Traditional workflows demand that analysts sift through disassembly, manually trace call graphs, and correlate data labels across multiple views. GhidraMCP abstracts these tasks behind a set of declarative tools, letting AI assistants perform the heavy lifting. This not only speeds up routine analysis but also democratizes access to advanced reverse‑engineering techniques, allowing security researchers and developers with limited Ghidra expertise to leverage its full power.

Key capabilities of the server include:

• Natural‑Language Querying: AI models can parse plain English questions and translate them into Ghidra API calls, returning structured results or decompiled snippets.
• Function & Data Manipulation: Tools such as and let assistants rename symbols on the fly, keeping the analysis workspace tidy.
• Security‑Focused Insights: Functions like , , and surface potential vulnerabilities, input sanitization gaps, or cryptographic primitives.
• Graph Generation: produces hierarchical call trees, helping analysts visualize control flow without leaving the AI chat.
• Cross‑Platform Compatibility: The server runs on any operating system supported by Ghidra, making it usable in diverse environments.

In practice, a security analyst could load a suspect executable into Ghidra, start the MCP server, and then ask an AI assistant “What are all the exported functions and their purposes?”. The assistant would invoke , parse the results, and present a concise summary. In an embedded systems context, developers might query “Which memory regions are writable and executable?” and receive a detailed memory map, enabling quick assessment of hardening needs. For malware research, the assistant can automatically identify obfuscated strings or trace API call chains that indicate malicious behavior.

Integration into AI workflows is seamless: the MCP server exposes a set of tools that any MCP‑compatible client can call. Once connected, the AI assistant behaves like a domain expert—parsing intent, retrieving data from Ghidra, and formatting responses—all without the analyst needing to touch the GUI. This synergy turns Ghidra from a static analysis tool into an interactive, AI‑augmented platform that accelerates discovery, reduces human error, and opens advanced reverse‑engineering capabilities to a broader audience.