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A production-ready Model Context Protocol (MCP) server that bridges Ghidra's powerful reverse engineering capabilities with modern AI tools and automation frameworks. 244 MCP tools, battle-tested AI workflows, and the most comprehensive Ghidra-MCP integration available — now including P-code emulation, live debugger integration, and PCode-graph data flow analysis.

Why Ghidra MCP?

Most Ghidra MCP implementations give you a handful of read-only tools and call it a day. This project is different — it was built by a reverse engineer who uses it daily on real binaries, not as a demo.

• 244 MCP tools — 3x more than any competing implementation. Not just read operations — full write access for renaming, typing, commenting, structure creation, script execution, P-code emulation, and live debugging.
• Battle-tested AI workflows — Proven documentation workflows (V5) refined across hundreds of functions. Includes step-by-step prompts, Hungarian notation reference, batch processing guides, and orphaned code discovery.
• Production-grade reliability — Atomic transactions, batch operations (93% API call reduction), configurable timeouts, and graceful error handling. No silent failures.
• Cross-binary documentation transfer — SHA-256 function hash matching propagates documentation across binary versions automatically. Document once, apply everywhere.
• Full Ghidra Server integration — Connect to shared Ghidra servers, manage repositories, version control, checkout/checkin workflows, and multi-user collaboration.
• Headless and GUI modes — Run with or without the Ghidra GUI. Docker-ready for CI/CD pipelines and automated analysis at scale.
• Opinionated by design — v5.0 moves naming conventions, type safety, and documentation standards into the tool layer. AI agents and human engineers produce consistent output without style guides in every prompt.

Convention Enforcement

You've been there: six months into a project you find ProcessItem, process_items, handleItem, and ItemProc in the same codebase — four functions doing the same thing, named by four different sessions or engineers with no shared contract. Fixing it takes longer than it should, and the problem will happen again.

v5.0 moves conventions from "things to remember" into the tool layer, where they can actually be enforced.

| Tier | Behavior | Example | |------|----------|---------| | Auto-fix | Applied silently | count field on a uint32 → auto-prefixed dwCount on save | | Warn | Change goes through, warning returned | processData → "name should be PascalCase with a verb: ProcessData" | | Reject | Change blocked with explanation | undefined → undefined type change → "no-op rejected, type unchanged" |

For AI agents, this means consistent output across every session, every model, every run — without pasting a style guide into every prompt. The tool knows the rules; the model just needs to make the call.

For teams, it eliminates the entire class of review comment that says "that's not our naming convention." Convention arbitration stays in the tool, not in code review.

For solo work at scale, analyze_function_completeness gives you a 0–100% score that measures honestly: structural deductions (unfixable compiler artifacts) are forgiven in your effective score, log-scaling prevents one bad category from burying everything else, and tiered plate comment quality means you know exactly what's missing and why.

🌟 Features

Core MCP Integration

• Full MCP Compatibility — Complete implementation of Model Context Protocol
• 244 MCP Tools — Comprehensive API surface covering every aspect of binary analysis
• Production-Ready Reliability — Atomic transactions, batch operations, configurable timeouts
• Real-time Analysis — Live integration with Ghidra's analysis engine

Compatibility note: MCP tool names are normalized for GitHub Copilot CLI and CAPI validation. Exposed tool names use lowercase letters, digits, underscores, and hyphens only; nested HTTP paths such as /debugger/status are advertised as names like debugger_status_2 when needed to avoid collisions with static bridge tools.

Binary Analysis Capabilities

• Function Analysis — Decompilation, call graphs, cross-references, completeness scoring
• Data Flow Analysis — PCode-graph value propagation (forward / backward) from any variable or register
• Data Structure Discovery — Struct/union/enum creation with field analysis and naming suggestions
• String Extraction — Regex search, quality filtering, and string-anchored function discovery
• Import/Export Analysis — Symbol tables, external locations, ordinal import resolution
• Memory & Data Inspection — Raw memory reads, byte pattern search, array boundary detection
• Cross-Binary Documentation — Function hash matching and documentation propagation across versions

Dynamic Analysis (v5.4.0)

• P-code Emulation — Run any function in isolation via Ghidra's EmulatorHelper; brute-force API hash resolution in milliseconds
• Live Debugger Integration — 17 Java endpoints + 22 Python bridge tools over Ghidra's TraceRmi framework (dbgeng on Windows PE, gdb/lldb otherwise): attach, step, breakpoints, registers, memory reads, non-breaking function tracing, ASLR-aware static↔dynamic address translation

AI-Powered Reverse Engineering Workflows

• Function Documentation Workflow V5 — 7-step process for complete function documentation with Hungarian notation, type auditing, and automated verification scoring
• Batch Documentation — Parallel subagent dispatch for documenting multiple functions simultaneously
• Orphaned Code Discovery — Automated scanner finds undiscovered functions in gaps between known code
• Data Type Investigation — Systematic workflows for structure discovery and field analysis
• Cross-Version Matching — Hash-based function matching across different binary versions

Development & Automation

• Ghidra Script Management — Create, run, update, and delete Ghidra scripts entirely via MCP
• Multi-Program Support — Switch between and compare multiple open programs
• Batch Operations — Bulk renaming, commenting, typing, and label management (93% fewer API calls)
• Headless Server — Full analysis without Ghidra GUI — Docker and CI/CD ready
• Project & Version Control — Create projects, manage files, Ghidra Server integration
• Analysis Control — List, configure, and trigger Ghidra analyzers programmatically

🚀 Quick Start

Prerequisites

• Java 21 LTS (OpenJDK recommended)
• Apache Maven 3.9+
• Ghidra 12.1 (or compatible version)
• Python 3.10+ with pip

Shared Ghidra Server users: Ghidra 12.1 clients require a Ghidra Server at 12.1, 12.0.5, or a newer compatible version. Upgrade the server before using this plugin from a 12.1 client.

Ghidra 12.1 ships Jython as an optional extension. Java scripts work by default, but .py scripts in ghidra_scripts/ require installing the Jython extension from File > Install Extensions and restarting Ghidra.

Installation

Recommended for all platforms: use python -m tools.setup directly.

ensure-prereqs installs runtime Python requirements plus the Ghidra JARs needed in the local Maven repository. deploy copies the build output, installs the user-profile extension, and patches Ghidra user config.

1. Clone the repository:

   git clone https://github.com/bethington/ghi