Ghidra MCP Server

Overview

The Ghidra MCP Server turns the powerful reverse‑engineering suite Ghidra into a lightweight, AI‑ready backend. By running Ghidra in headless mode it extracts comprehensive analysis artifacts—functions, pseudocode, data structures, enums and more—from a binary into a single JSON file. The MCP server then exposes this data through a set of intuitive tools that an LLM can call directly, enabling developers to ask natural‑language questions about the binary and receive structured, actionable answers.

This approach solves a common pain point for security researchers and software developers: the need to manually parse Ghidra’s output or write custom parsers for each analysis artifact. Instead of juggling a GUI, scripting the decompiler, and extracting results, the server automates the entire pipeline. Once a binary is fed to , all subsequent queries are fast, stateless calls that return JSON objects. This reduces cognitive load and speeds up the feedback loop when iterating on reverse‑engineering tasks.

Key capabilities of the Ghidra MCP Server include:

• Function discovery – returns every decompiled function, while delivers the full pseudocode for a specific entry.
• Data model introspection – and expose all structs, their fields, sizes and alignment; similarly and provide enum definitions.
• Prototype extraction – and return function signatures, including return types and argument lists.
• Context setup – orchestrates the headless Ghidra run, ensuring that all artifacts are refreshed whenever a new binary is analyzed.

Typical use cases span from automated vulnerability discovery to documentation generation. A security analyst can ask the AI, “What does the entry point do?” and receive a concise summary of the main function’s logic. A developer maintaining legacy code can query “Show me all structs used by this module” and instantly get a detailed list without opening Ghidra. In CI pipelines, the server can be invoked to validate that no new functions or data structures have been introduced in a binary build, aiding regression testing.

Integration with AI workflows is straightforward: an MCP‑compatible client (such as Claude Desktop) registers the server via a simple command, after which the AI can invoke any of the exposed tools as part of its reasoning process. The server’s JSON responses feed directly into prompts, allowing the model to reference concrete data while formulating explanations or generating code snippets. This tight coupling eliminates the need for manual copy‑and‑paste and ensures that the AI’s knowledge of the binary is always up‑to‑date.

What sets this server apart is its end‑to‑end automation and the breadth of data it surfaces. By bridging Ghidra’s rich analysis with the conversational power of LLMs, developers gain a powerful ally that turns static binaries into interactive knowledge bases—streamlining reverse engineering, security assessment, and documentation with minimal friction.