bethington/ghidra-mcp

Ghidra MCP Server — 200+ MCP tools for AI-powered reverse engineering. GUI plugin + headless server, lazy tool loading, convention enforcement, batch...

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Summary Information

Updated 5 minutes ago
Added to GitGenius on September 17th, 2026
Created on August 30th, 2025
Open Issues & Pull Requests: 41 (+0)
GitHub issues: Enabled
Number of forks: 149
Total Stargazers: 3,926 (+0)
Total Subscribers: 10 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 19.1 hours
Mean response time: 2.7 days
90th percentile: 7.1 days
Tracked items: 108

Most active contributors

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How this project is maintained

About 15% of issues opened in the past year have never received a reply. 50% of open issues come from outside the core team, a mix of external reports and the maintainers' own roadmap. 76% of issues opened in the past year have been closed, leaving a working backlog. Three people close 86% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 30
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 37 days
Stale 30+ days: 12
Stale 90+ days: 3

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • enhancement (11)
  • bug (2)

Most active issues this week

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Detailed Description

Ghidra MCP Server is an MCP server that exposes reverse engineering capabilities from Ghidra to AI models through the Model Context Protocol.

The tool solves the problem of integrating Ghidra's binary analysis features with AI-powered workflows. It works by implementing an MCP server that makes Ghidra's functionality available as tools that language models can call. The server can run as a GUI plugin within Ghidra or as a headless server, and it implements lazy tool loading to avoid unnecessary initialization overhead. The tool set includes batch operations and integrates with Ghidra Server for collaborative analysis scenarios.

Developers working on reverse engineering tasks who want to leverage AI assistance should consider this tool. It suits projects where binary analysis needs to be automated or augmented with AI reasoning. The headless server mode makes it suitable for integration into CI/CD pipelines or containerized environments, with Docker deployment support included. The convention enforcement and batch operation capabilities suggest it is designed for reliable, repeatable analysis workflows rather than ad-hoc exploration.

The project shows active development with regular commits addressing both feature additions and bug fixes. Pull requests are reviewed and merged with attention to code quality. The maintainer responds to issues and incorporates user feedback into the codebase. Documentation is maintained alongside code changes to reflect the current state of the tool.