Content
<p align="center">
<img src="https://github.com/user-attachments/assets/31c1831a-5be1-4698-8171-5ebfc9d6797c" width=60% >
</p>
<p align="center">
<img align="center" alt="GitHub Workflow Status (with event)" src="https://img.shields.io/github/actions/workflow/status/clearbluejar/pyghidra-mcp/pytest-devcontainer-repo-all.yml?style=for-the-badge&label=pytest">
<img align="center" alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/pyghidra-mcp?color=yellow&label=PyPI%20downloads&style=for-the-badge">
<img align="center" src="https://img.shields.io/github/stars/clearbluejar/pyghidra-mcp?style=for-the-badge">
</p>
# PyGhidra-MCP - Ghidra Model Context Protocol Server
### Overview
**`pyghidra-mcp`** is a command-line Model Context Protocol (MCP) server that brings the full analytical power of [Ghidra](https://ghidra-sre.org/), a robust software reverse engineering (SRE) suite, into the world of intelligent agents and LLM-based tooling.
It bridges Ghidra’s [ProgramAPI](https://ghidra.re/ghidra_docs/api/ghidra/program/model/listing/Program.html) and [FlatProgramAPI](https://ghidra.re/ghidra_docs/api/ghidra/program/flatapi/FlatProgramAPI.html) to Python using `pyghidra` and `jpype`, then exposes that functionality via the Model Context Protocol.
MCP is a unified interface that allows language models, development tools (like VS Code), and autonomous agents to access structured context, invoke tooling, and collaborate intelligently. Think of MCP as the bridge between powerful analysis tools and the LLM ecosystem.
With `pyghidra-mcp`, Ghidra becomes an intelligent backend—ready to respond to context-rich queries, automate deep reverse engineering tasks, and integrate into AI-assisted workflows.
> [!NOTE]
> This beta project is under active development. We would love your feedback, bug reports, feature requests, and code.
## Yet another Ghidra MCP?
Yes, the original [ghidra-mcp](https://github.com/LaurieWired/GhidraMCP) is fantastic. But `pyghidra-mcp` takes a different approach:
- 🐍 **No GUI required** – Run entirely via CLI for streamlined automation and scripting.
- 🔁 **Designed for automation** – Ideal for integrating with LLMs, CI pipelines, and tooling that needs repeatable behavior.
- ✅ **CI/CD friendly** – Built with robust unit and integration tests for both client and server sessions.
- 🚀 **Quick startup** – Asynchronous startup allows the server to start handling requests while binaries are still being analyzed in the background. Supports fast command-line launching with minimal setup.
- 📦 **Project-wide analysis** – Enables concurrent reverse engineering of all binaries in a Ghidra project
- 🤖 **Agent-ready** – Built for intelligent agent-driven workflows and large-scale reverse engineering automation.
- 🔍 Semantic code search – Uses vector embeddings (via ChromaDB) to enable fast, fuzzy lookup across decompiled functions, comments, and symbols—perfect for pseudo-C exploration and agent-driven triage.
This project provides a Python-first experience optimized for local development, headless environments, and testable workflows.
```mermaid
graph TD
subgraph Clients
A[🤖 LLM / Agent]
B[💻 Local CLI User]
C[🔧 CI/CD Pipeline]
end
subgraph Startup Command
direction LR
cmd("`pyghidra-mcp --project-path /path/to/project --threaded /path/to/binary1`")
end
subgraph "pyghidra-mcp Server"
D[MCP Server]
E[pyghidra ]
F[Ghidra Headless]
subgraph "Ghidra Project Analysis"
G[Binary 1]
H[Binary 2]
I[...]
end
end
cmd --> D
A -- "MCP (stdio/http)" --> D
B -- "stdio/http" --> D
C -- "stdio/sse" --> D
D -- "Initializes" --> E
E -- "Controls" --> F
F -- "Analyzes Concurrently" --> G
F -- "Analyzes Concurrently" --> H
F -- "Analyzes Concurrently" --> I
subgraph "Exposed MCP API"
J[decompile_function]
end
D -- "Exposes Tools" --> J
J -- "Results" --> A
```
## Contents
- [PyGhidra-MCP - Ghidra Model Context Protocol Server](#pyghidra-mcp---ghidra-model-context-protocol-server)
- [Overview](#overview)
- [Yet another Ghidra MCP?](#yet-another-ghidra-mcp)
- [Contents](#contents)
- [Getting started](#getting-started)
- [CLI Client](#cli-client)
- [Project Creation, Management, and Opening Existing Projects](#project-creation-management-and-opening-existing-projects)
- [Creating New Projects](#creating-new-projects)
- [Self-Contained Project Structure](#self-contained-project-structure)
- [Basic Project Creation](#basic-project-creation)
- [Custom Project Creation](#custom-project-creation)
- [Creating Multiple Related Projects](#creating-multiple-related-projects)
- [Opening Existing Ghidra Projects](#opening-existing-ghidra-projects)
- [Opening by .gpr File](#opening-by-gpr-file)
- [Development](#development)
- [Setup](#setup)
- [Testing and Quality](#testing-and-quality)
- [API](#api)
- [Tools](#tools)
- [Code Search](#code-search)
- [Cross-References](#cross-references)
- [Generate Call Graph](#generate-call-graph)
- [Decompile Function](#decompile-function)
- [Import Binary](#import-binary)
- [List Exports](#list-exports)
- [List Imports](#list-imports)
- [List Project Binaries](#list-project-binaries)
- [List Project Binary Metadata](#list-project-binary-metadata)
- [Delete Project Binary](#delete-project-binary)
- [Read Bytes](#read-bytes)
- [Search Strings](#search-strings)
- [Search Symbols](#search-symbols)
- [Prompts](#prompts)
- [Resources](#resources)
- [Usage](#usage)
- [Mapping Binaries with Docker](#mapping-binaries-with-docker)
- [Using with OpenWeb-UI and MCPO](#using-with-openweb-ui-and-mcpo)
- [With `uvx`](#with-uvx)
- [With Docker](#with-docker)
- [Standard Input/Output (stdio)](#standard-inputoutput-stdio)
- [Python](#python)
- [Docker](#docker)
- [Streamable HTTP](#streamable-http)
- [Python](#python-1)
- [Docker](#docker-1)
- [Server-sent events (SSE)](#server-sent-events-sse)
- [Python](#python-2)
- [Docker](#docker-2)
- [Integrations](#integrations)
- [Claude Desktop](#claude-desktop)
- [Inspiration](#inspiration)
- [Contributing, community, and running from source](#contributing-community-and-running-from-source)
- [Contributor workflow](#contributor-workflow)
## Getting started
Run the [Python package](https://pypi.org/p/pyghidra-mcp) as a CLI command using [`uv`](https://docs.astral.sh/uv/guides/tools/):
```bash
uvx pyghidra-mcp # Creates pyghidra_mcp_projects directory by default
```
Or, run as a [Docker container](https://ghcr.io/clearbluejar/pyghidra-mcp):
```bash
docker run -i --rm ghcr.io/clearbluejar/pyghidra-mcp -t stdio
```
## CLI Client
For a more interactive command-line experience, you can use the separate **pyghidra-mcp-cli** package, which provides a user-friendly interface for interacting with a running pyghidra-mcp server.
### Installation
Install the CLI client using [`uv`](https://docs.astral.sh/uv/) (recommended):
```bash
uvx pyghidra-mcp-cli
```
Or install with pip:
```bash
pip install pyghidra-mcp-cli
```
### Quick Start with CLI
1. **Start the server** (in one terminal):
```bash
pyghidra-mcp --transport streamable-http /bin/ls
```
2. **Use the CLI** (in another terminal):
```bash
# List available binaries
pyghidra-mcp-cli list binaries
# Decompile a function
pyghidra-mcp-cli decompile --binary ls main
# Search for symbols
pyghidra-mcp-cli search symbols --binary ls printf -l 10
```
> [!NOTE]
> The CLI connects to pyghidra-mcp via HTTP to avoid the 10-60 second startup overhead of spawning a new Ghidra process for each command. See the [CLI README](./cli/README.md) for complete documentation.
## Project Creation, Management, and Opening Existing Projects
### Creating New Projects
You can create new projects in several ways, depending on your workflow:
#### Self-Contained Project Structure
`pyghidra-mcp` creates a **self-contained project structure** where each project has its own Ghidra project and pyghidra-mcp artifacts. This ensures complete isolation and easy project management.
#### Basic Project Creation
```bash
# Create a new project with default settings
pyghidra_mcp
# Creates:
$ tree pyghidra_mcp_projects/
pyghidra_mcp_projects/
├── my_project.gpr
├── my_project-pyghidra-mcp
│ ├── chromadb
│ └── gzfs
└── my_project.rep
```
#### Custom Project Creation
```bash
# Create project with custom name and location
pyghidra_mcp --project-path ~/analysis/malware_study --project-name malware_analysis
$ tree ~/analysis/
/home/vscode/analysis/
└── malware_study
├── malware_analysis.gpr
├── malware_analysis-pyghidra-mcp
│ ├── chromadb
│ └── gzfs
└── malware_analysis.rep
```
#### Creating Multiple Related Projects
```bash
# Create separate projects for different analysis focuses
mkdir ~/reverse_engineering_workspace
# Project for suspicious binaries
pyghidra_mcp --project-path ~/reverse_engineering_workspace/suspicious_binaries --project-name suspicious_analysis
# Project for packed malware
pyghidra_mcp --project-path ~/reverse_engineering_workspace/packed_malware --project-name packed_analysis
```
### Opening Existing Ghidra Projects
If you have existing Ghidra projects (`.gpr` files), you can open them directly with `pyghidra-mcp`:
#### Opening by .gpr File
```bash
# Open existing Ghidra project (project name derived from filename)
pyghidra_mcp --project-path ~/existing/ghidra/my_research.gpr
# Result: ~/existing/ghidra/my_research-pyghidra-mcp/
# └── chromadb/, gzfs/ (pyghidra-mcp additions)
```
## Development
This project uses a `Makefile` to streamline development and testing. `ruff` is used for linting and formatting, and `pre-commit` hooks are used to ensure code quality.
### Setup
1. **Install `uv`**: If you don't have `uv` installed, you can install it using pip:
```bash
pip install uv
```
Or, follow the official `uv` installation guide: [https://docs.astral.sh/uv/install/](https://docs.astral.sh/uv/install/)
2. **Create a virtual environment and install dependencies**:
```bash
make dev-setup
source ./.venv/bin/activate
```
3. **Set Ghidra Environment Variable**: Download and install Ghidra, then set the `GHIDRA_INSTALL_DIR` environment variable to your Ghidra installation directory.
```bash
# For Linux / Mac
export GHIDRA_INSTALL_DIR="/path/to/ghidra/"
# For Windows PowerShell
[System.Environment]::SetEnvironmentVariable('GHIDRA_INSTALL_DIR','C:\ghidra_10.2.3_PUBLIC_20230208\ghidra_10.2.3_PUBLIC')
```
### Testing and Quality
The `Makefile` provides several targets for testing and code quality:
- `make run`: Run the MCP server.
- `make test`: Run the full test suite (unit and integration).
- `make test-unit`: Run unit tests.
- `make test-integration`: Run integration tests.
- `make lint`: Check code style with `ruff`.
- `make format`: Format code with `ruff`.
- `make typecheck`: Run type checking with `ruff`.
- `make check`: Run all quality checks.
- `make dev`: Run the development workflow (format and check).
- `make build`: Build distribution packages.
- `make clean`: Clean build artifacts and cache.
## API
### Tools
Enable LLMs to perform actions, make deterministic computations, and interact with external services.
#### Code Search
- `search_code(binary_name: str, query: str, limit: int = 5)`: Search for code within a binary by similarity using vector embeddings.
#### Cross-References
- `list_cross_references(binary_name: str, name_or_address: str)`: Finds and lists all cross-references (x-refs) to a given function or address.
#### Generate Call Graph
- `gen_callgraph(binary_name: str, function_name_or_address: str, direction: str = "calling", display_type: str = "flow", include_refs: bool = True, max_depth: int | None = None, max_run_time: int = 60, condense_threshold: int = 50, top_layers: int = 5, bottom_layers: int = 5)`: Generates a MermaidJS call graph for a specified function. Supports both "calling" (functions called by the target) and "called" (functions that call the target) directions with multiple visualization types.
#### Decompile Function
- `decompile_function(binary_name: str, name: str)`: Decompile a function from a given binary.
#### Import Binary
- `import_binary(binary_path: str)`: Imports a binary from a designated path into the current Ghidra project. If the path is a directory, it will recursively scan and import all supported binary files, preserving the directory structure within the Ghidra project.
#### List Exports
- `list_exports(binary_name: str, query: str = ".*", offset: int = 0, limit: int = 25)`: Lists all exported functions and symbols from a specified binary (regex supported for query).
#### List Imports
- `list_imports(binary_name: str, query: str = ".*", offset: int = 0, limit: int = 25)`: Lists all imported functions and symbols for a specified binary (regex supported for query).
#### List Project Binaries
- `list_project_binaries()`: Lists the names of all binaries currently loaded in the Ghidra project.
#### List Project Binary Metadata
- `list_project_binary_metadata(binary_name: str)`: Retrieves detailed metadata for a specific binary, including architecture, compiler, executable format, analysis metrics, and file hashes.
#### Delete Project Binary
- `delete_project_binary(binary_name: str)`: Deletes a binary (program) from the Ghidra project.
#### Read Bytes
- `read_bytes(binary_name: str, address: str, size: int = 32)`: Reads raw bytes from memory at a specified address. Returns raw hex data. Useful for inspecting memory contents, data structures, or confirming analysis findings.
#### Search Strings
- `search_strings(binary_name: str, query: str, limit: int = 100)`: Searches for strings within a binary by name.
#### Search Symbols
- `search_symbols_by_name(binary_name: str, query: str, offset: int = 0, limit: int = 25)`: Search for symbols within a binary by name (case-insensitive substring).
### Prompts
Reusable prompts to standardize common LLM interactions.
- `write_ghidra_script`: Return a prompt to help write a Ghidra script.
### Resources
Expose data and content to LLMs
- `ghidra://program/{program_name}/function/{function_name}/decompiled`: Decompiled code of a specific function.
## Usage
This Python package is published to PyPI as [pyghidra-mcp](https://pypi.org/p/pyghidra-mcp) and can be installed and run with [pip](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#install-a-package), [pipx](https://pipx.pypa.io/), [uv](https://docs.astral.sh/uv/), [poetry](https://python-poetry.org/), or any Python package manager.
```text
$ uvx pyghidra-mcp --help
Usage: pyghidra-mcp [OPTIONS] [INPUT_PATHS]...
PyGhidra Command-Line MCP server
- input_paths: Path to one or more binaries to import, analyze, and expose with pyghidra-mcp
- transport: Supports stdio, streamable-http, and sse transports.
For stdio, it will read from stdin and write to stdout.
For streamable-http and sse, it will start an HTTP server on the specified port (default 8000).
Options:
-v, --version Show version and exit.
-t, --transport [stdio|streamable-http|sse]
Transport protocol to use: stdio,
streamable-http, or sse (legacy).
--project-path PATH Location on disk which points to the Ghidra
project to use. Can be an existing file.
[default: pyghidra_mcp_projects]
--project-name TEXT Name for the project (used for Ghidra project
files). Ignored when using .gpr files.
[default: my_project]
-p, --port INTEGER Port to listen on for HTTP-based transports
(streamable-http, sse). [default: 8000]
-o, --host TEXT Host to listen on for HTTP-based transports
(streamable-http, sse). [default: 127.0.0.1]
--threaded / --no-threaded Allow threaded analysis. Disable for debug.
[default: threaded]
--force-analysis / --no-force-analysis
Force a new binary analysis each run.
[default: no-force-analysis]
--verbose-analysis / --no-verbose-analysis
Verbose logging for analysis step.
[default: no-verbose-analysis]
--no-symbols / --with-symbols Turn off symbols for analysis.
[default: no-symbols]
--gdt PATH Path to a GDT file for analysis. Can be
specified multiple times.
--program-options PATH Path to a JSON file containing program
options (custom analyzer settings).
--gzfs-path PATH Location to store GZFs of analyzed
binaries.
--max-workers INTEGER Number of workers for threaded analysis.
Defaults to CPU count. [default: 0]
--wait-for-analysis / --no-wait-for-analysis
Wait for initial project analysis to
complete before starting the server.
[default: no-wait-for-analysis]
-h, --help Show this message and exit.
```
### Mapping Binaries with Docker
When using the Docker container, you can map a local directory containing your binaries into the container's workspace. This allows `pyghidra-mcp` to analyze your files.
```bash
# Create and populate the new directory
mkdir -p ./binaries
cp /path/to/your/binaries/* ./binaries/
# Run the Docker container with volume mapping
docker run -i --rm \
-v "$(pwd)/binaries:/binaries" \
ghcr.io/clearbluejar/pyghidra-mcp \
/binaries/*
```
### Using with OpenWeb-UI and MCPO
You can integrate `pyghidra-mcp` with [OpenWeb-UI](https://github.com/open-webui/open-webui) using [MCPO](https://github.com/open-webui/mcpo), an MCP-to-OpenAPI proxy. This allows you to expose `pyghidra-mcp`'s tools through a standard RESTful API, making them accessible to web interfaces and other tools.
https://github.com/user-attachments/assets/3d56ea08-ed2d-471d-9ed2-556fb8ee4c95
#### With `uvx`
You can run `pyghidra-mcp` and `mcpo` together using `uvx`:
```bash
uvx mcpo -- \
pyghidra-mcp /bin/ls
```
#### With Docker
You can combine mcpo with Docker:
```bash
uvx mcpo -- docker run ghcr.io/clearbluejar/pyghidra-mcp /bin/ls
```
### Standard Input/Output (stdio)
The stdio transport enables communication through standard input and output streams. This is particularly useful for local integrations and command-line tools. See the [spec](https://modelcontextprotocol.io/docs/concepts/transports#built-in-transport-types) for more details.
#### Python
```bash
pyghidra-mcp
```
By default, the Python package will run in `stdio` mode. Because it's using the standard input and output streams, it will look like the tool is hanging without any output, but this is expected.
#### Docker
This server is published to Github's Container Registry ([ghcr.io/clearbluejar/pyghidra-mcp](http://ghcr.io/clearbluejar/pyghidra-mcp))
```
docker run -i --rm ghcr.io/clearbluejar/pyghidra-mcp -t stdio
```
By default, the Docker container is in `SSE` mode, so you will have to include `-t stdio` after the image name and run with `-i` to run in [interactive](https://docs.docker.com/reference/cli/docker/container/run/#interactive) mode.
### Streamable HTTP
Streamable HTTP enables streaming responses over JSON RPC via HTTP POST requests. See the [spec](https://modelcontextprotocol.io/specification/draft/basic/transports#streamable-http) for more details.
By default, the server listens on [127.0.0.1:8000/mcp](https://127.0.0.1/mcp) for client connections. To change any of this, set [FASTMCP_*](https://github.com/modelcontextprotocol/python-sdk/blob/main/src/mcp/server/fastmcp/server.py#L78) environment variables. _The server must be running for clients to connect to it._
#### Python
```bash
pyghidra-mcp -t streamable-http
```
By default, the Python package will run in `stdio` mode, so you will have to include `-t streamable-http`.
#### Docker
```
docker run -p 8000:8000 ghcr.io/clearbluejar/pyghidra-mcp
```
### Server-sent events (SSE)
> [!WARNING]
> The MCP communiity considers this a legacy transport portcol and is really intended for backwards compatibility. [Streamable HTTP](#streamable-http) is the recommended replacement.
SSE transport enables server-to-client streaming with Server-Send Events for client-to-server and server-to-client communication. See the [spec](https://modelcontextprotocol.io/docs/concepts/transports#server-sent-events-sse) for more details.
By default, the server listens on [127.0.0.1:8000/sse](https://127.0.0.1/sse) for client connections. To change any of this, set [FASTMCP_*](https://github.com/modelcontextprotocol/python-sdk/blob/main/src/mcp/server/fastmcp/server.py#L78) environment variables. _The server must be running for clients to connect to it._
#### Python
```bash
pyghidra-mcp -t sse
```
By default, the Python package will run in `stdio` mode, so you will have to include `-t sse`.
#### Docker
```
docker run -p 8000:8000 ghcr.io/clearbluejar/pyghidra-mcp -t sse
```
## Integrations
> [!NOTE]
> This section is a work in progress. We will be adding examples for specific integrations soon.
### Claude Desktop
Add the following JSON block to your `claude_desktop_config.json` file:
```json
{
"mcpServers": {
"pyghidra-mcp": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/clearbluejar/pyghidra-mcp",
"pyghidra-mcp",
"--project-path",
"/tmp/pyghidra", // or path to writeable directory
"/bin/ls" //
],
"env": {
"GHIDRA_INSTALL_DIR": "/path/to/ghidra/ghidra_12.0_PUBLIC"
}
}
}
}
```
## Inspiration
This project implementation and design was inspired by these awesome projects:
* [GhidraMCP](https://github.com/lauriewired/GhidraMCP)
* [semgrep-mcp](https://github.com/semgrep/mcp)
* [ghidrecomp](https://github.com/clearbluejar/ghidrecomp)
* [BinAssistMCP](https://github.com/jtang613/BinAssistMCP)
---
## Contributing, community, and running from source
We believe the future of reverse engineering is agentic, contextual, and scalable.
`pyghidra-mcp` is a step toward that future—making full Ghidra projects accessible to AI agents and automation pipelines.
We’re actively developing the project and welcome feedback, issues, and contributions.
> [!NOTE]
> We love your feedback, bug reports, feature requests, and code.
### Contributor workflow
If you're adding a new tool or integration, here’s the recommended workflow:
- Label your branch with the prefix `feature/` to indicate a new capability.
- Add your tool using the same style and structure as existing tools in `pyghidra/tools/`.
- Write an integration test that exercises your tool using a `StdioClient` instance. Place it in `tests/integration/`.
- Extend concurrent testing by adding a call to your tool in `tests/integration/test_concurrent_streamable_client.py`.
- Run make test and make format to ensure your changes pass all tests and conform to linting rules.
This ensures consistency across the codebase and helps us maintain robust, scalable tooling for reverse engineering workflows.
______________________________________________________________________
Made with ❤️ by the [PyGhidra-MCP Team](https://github.com/clearbluejar/pyghidra-mcp)
