rz-mcp_server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@rz-mcp_serverInvestigate why Rizin crashes on this binary and write a bug report"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Rizin Debugger Bug-Hunting Agent
An AI agent (Goose) that hunts for bugs in the Rizin debugger by writing small test programs, compiling and running them inside a disposable QEMU VM, and investigating anything that looks wrong.
How it works
main.py
1. Boots a QEMU VM (installing it once if it doesn't exist yet)
2. Waits until the VM is reachable
3. Writes VM connection info to data/vm_connection.json
4. Launches Goose with prompts/investigate.yaml
Goose (the agent)
- Has zero capabilities of its own
- Can only call tools exposed by mcp_server/tools.py
- Reasons about what to try; tools.py does the actual work
tools.py (MCP server, runs as a Goose extension)
- discover_capabilities / get_vm_environment -> orient the agent
- create_test_json -> register a new test, get a test_id
- build_binary / test_binary -> compile + run a probe binary on the VM (SSH)
- get_binary_context -> real symbols/addresses via Rizin's own analysis
- update_test_commands / run_test -> run Rizin commands, capture output
- read_source_file / search_rizin_source -> investigate Rizin's own source
- create_report / submit_report -> write the final Markdown reportEvery test is tracked by a test_id; the agent never has to remember or
retype long values (binary paths, addresses) itself — it just passes the
ID and the tool looks up the rest.
Related MCP server: Binary MCP Server
Directory layout
main.py deterministic VM setup + launches Goose
scripts/ install.sh (one-time OS install), run.sh (boot existing image)
mcp_server/tools.py the agent's tools (MCP server)
prompts/investigate.yaml the agent's mission + which tools it can use
data/ vm_connection.json (host/port/user/pass, written by main.py)
tests/ one JSON file per test_id (commands, binary path, etc.)
context/ rizin-debugger-commands reference
reports/ final report.md gets saved here
rizin/ local Rizin source tree (for read_source_file / search_rizin_source)
vm_config.json VM's OS/arch/compiler info, given to the agent
images/ the QEMU disk image(s)Running it
goose configure # one-time: set LLM provider + register tools.py as an extension
python main.py # boots the VM and runs one investigationOutput lands in reports/report.md.
Current scope
One test investigation per run (no multi-test loop yet).
The agent can only investigate and hypothesize — it cannot modify Rizin's source or fix anything.
VM setup is deterministic (
main.py/shell scripts); the agent never provisions infrastructure itself.SSH auth currently uses a fixed password (dev-only setup, not for shared/production use).
This server cannot be deployed
Maintenance
Related MCP Connectors
Hunt zero-days by talking to binaries. 40+ tools. Hosted, OAuth + SSO, invite: hi@byteray.ai
Browser-backed QA with evidence and fix-ready reports for coding agents.
Agentic CI operations for build inspection, failure diagnosis, and runner troubleshooting.
Shared debugging memory for AI coding agents
Related MCP Servers
- FlicenseAqualityDmaintenanceEnables AI agents to debug embedded systems by providing a comprehensive interface for GDB operations across multiple architectures like ARM and x86. It supports remote debugging via gdbserver or QEMU, allowing for detailed inspection of memory, registers, stack frames, and variables.31-
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to analyze binaries, debug processes, and inspect kernel state using Ghidra, x64dbg, WinDbg, and ILSpyCmd.7Apache 2.0
- FlicenseNot gradedqualityCmaintenanceA stateful debugging and binary research system for LLM agents, integrating GDB + pwndbg with MCP for deterministic exploit workflows and multi-session support.281-
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to capture, replay, inspect, diff, share, and manage executable bug artifacts (.bug files) via the BugProof CLI, supporting 10 tools and structured prompts for debugging workflows.31AGPL 3.0