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reverse-engineering-assistant MCP Server

cyberkaida/reverse-engineering-assistant

Score: 75.0 ↑ 4.3% Rank #3110 MCP Server
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MCP server for reverse engineering tasks in Ghidra πŸ‘©β€πŸ’»

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Overview

cyberkaida/reverse-engineering-assistant is a Java MCP server licensed under Apache-2.0. MCP server for reverse engineering tasks in Ghidra πŸ‘©β€πŸ’» Topics: assistant, binaryninja, ghidra, llm, reverse-engineering, mcp, mcp-server.

Ranked #3110 out of 24846 indexed tools.

Has 14 contributors.

Ecosystem

Java Apache-2.0
assistantbinaryninjaghidrallmreverse-engineeringmcpmcp-server

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 871

871 stars β†’ growing community

Freshness 25% 15d ago

Last commit 15d ago β†’ recently updated

Issue Health 25% 87%

97/112 issues closed β†’ responsive maintainer

Contributors 10% 14

14 contributors β†’ active community

Dependents 25% 2

2 dependents β†’ few downstream users

npm Downloads N/A
PyPI Downloads N/A
Forks 78
Description Good
License Apache-2.0

Weights: Freshness 20% Β· Issue Health 20% Β· Dependents 22% Β· Stars 10% Β· Contributors 8% Β· How we score β†’

How to Improve

Description low impact

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Matched Queries

"mcp server""mcp-server"topic:mcptopic:mcp-server

From the README

# ReVa - Ghidra MCP Server for AI-Powered Reverse Engineering

> A Ghidra extension that provides a [Model Context Protocol (MCP)](https://modelcontextprotocol.io/faqs) server for AI-assisted reverse engineering

ReVa (Reverse Engineering Assistant) is a **Ghidra MCP server** that enables AI language models to interact with Ghidra's powerful reverse engineering capabilities. ReVa uses
state of the art techniques to limit [context rot](https://github.com/chroma-core/context-rot) and enable
long form reverse engineering tasks.

ReVa is different from other efforts at building AI assistants for RE tasks because it uses a _tool driven approach_ with a focus
on designing tools for effective LLM use.
ReVa aims to provide a variety of small tools to the LLM, just as your RE environment provides a set of small tools
to you.

Each of the tools given to the LLM are constructed to be easy for the LLM to use and to tolerate a variety of inputs
and to reduce hallucination by the LLM. We do this by 
Read full README on GitHub →

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