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binary-ninja-headless-mcp MCP Server

mrphrazer/binary-ninja-headless-mcp

Score: 59.7 ↑ 14.4% Rank #6409 MCP Server
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Headless Binary Ninja MCP server — giving AI agents deep reverse-engineering capabilities via 180 tools.

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Overview

mrphrazer/binary-ninja-headless-mcp is a Python MCP server licensed under GPL-2.0. Headless Binary Ninja MCP server — giving AI agents deep reverse-engineering capabilities via 180 tools.

Ranked #6409 out of 24846 indexed tools.

Ecosystem

Python GPL-2.0

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 256

256 stars → growing community

Freshness 25% 1mo ago

Last commit 1mo ago → moderate activity

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 4

4 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 23
Description Good
License GPL-2.0

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Freshness high impact

Last commit was 39 days ago — a recent commit would boost your freshness score

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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AgentRank score for mrphrazer/binary-ninja-headless-mcp
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Matched Queries

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From the README

# Binary Ninja Headless MCP

A headless [Binary Ninja](https://binary.ninja/) server that speaks [MCP](https://modelcontextprotocol.io/) (Model Context Protocol), giving AI agents full access to deep reverse-engineering workflows — disassembly, IL, patching, types, xrefs, and more — without a GUI.

Designed to run in the same Docker container as the agent runtime. No sidecars, no extra services.

This entire project---code, tests, and documentation---is 100% vibe coded.

## Why

Existing Binary Ninja MCP servers are either GUI-bound or expose a limited tool surface. This server is headless-only and designed for agent-driven workflows in sandboxed VM/container environments: the agent gets full control over the analysis system, automating large parts of reverse engineering while you interactively discuss and steer the process.

The goal is an interface where agents can inspect, refine, and extend an analysis over time — updating types, symbols, and metadata, improving the analysis databa
Read full README on GitHub →

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