karellen-rr-mcp

karellen-rr-mcp

Enables LLM-powered debugging with rr reverse execution, allowing record and replay of program execution via GDB/MI for forward and reverse state inspection.

Category
Visit Server

README

MCP Server for rr Reverse Debugging (karellen-rr-mcp)

Gitter Build Status Coverage Status

karellen-rr-mcp Version karellen-rr-mcp Python Versions karellen-rr-mcp Downloads Per Day karellen-rr-mcp Downloads Per Week karellen-rr-mcp Downloads Per Month

Overview

karellen-rr-mcp is an MCP (Model Context Protocol) server that enables any MCP-compliant LLM client to use rr for reverse debugging. Instead of iteratively adding debug output and rebuilding, the LLM can record a failing test with rr, then replay it with full forward and reverse debugging via GDB/MI, inspecting program state without modifying source code.

Requirements

  • Linux on x86-64 (rr only supports Linux; aarch64 is experimental)
  • rr installed and on PATH
  • GDB installed and on PATH (used by rr for debugging)
  • Python >= 3.10
  • perf_event_paranoid set to 1 to allow recording:
    sudo sysctl kernel.perf_event_paranoid=1
    

Installing rr and GDB

Via pip (using karellen-rr):

pip install karellen-rr-mcp[rr]

This installs rr as a pip package alongside karellen-rr-mcp. GDB must still be installed separately via your system package manager.

Fedora / RHEL / CentOS:

sudo dnf install rr gdb

Ubuntu / Debian:

sudo apt install rr gdb

Arch Linux:

sudo pacman -S rr gdb

Configuring perf_event_paranoid

rr requires access to hardware performance counters. Set perf_event_paranoid to 1:

sudo sysctl kernel.perf_event_paranoid=1

To make this persistent across reboots:

echo 'kernel.perf_event_paranoid=1' | sudo tee /etc/sysctl.d/50-rr.conf

Verify the setup

rr record /bin/true && echo "rr is working"

If this fails with a permissions error, check perf_event_paranoid. If it fails inside a container or VM, note that rr requires access to CPU performance counters — it does not work in most containers (Docker, Podman) or VMs unless hardware PMU passthrough is configured.

Installation

pip install karellen-rr-mcp

Or with pipx for an isolated environment:

pipx install karellen-rr-mcp

Claude Code Integration

Claude Code plugin (recommended)

The plugin automatically configures the MCP server and includes:

  • Crash detection hook that suggests rr when a Bash command exits with a signal (SIGSEGV, SIGABRT, SIGBUS, etc.) or output contains crash/sanitizer signatures
  • /karellen-rr-mcp:rr-debug skill that walks through the full record-replay-analyze workflow step by step
  • rr-investigator agent that Claude can spawn to autonomously investigate crashes using rr reverse execution

From the Karellen plugins marketplace:

claude plugin marketplace add karellen/claude-plugins
claude plugin install karellen-rr-mcp@karellen-plugins

Or from the official Anthropic marketplace (if accepted):

claude plugin install karellen-rr-mcp@claude-plugins-official

Or load directly from a local checkout for testing:

claude --plugin-dir /path/to/karellen-rr-mcp

Manual MCP server configuration

If you prefer not to use the plugin, you can configure the MCP server directly. This gives you the MCP tools but not the skill, agent, or crash detection hook.

Using the CLI:

claude mcp add --transport stdio karellen-rr-mcp -- karellen-rr-mcp

Or manually add to ~/.claude.json (user scope) or .mcp.json in your project root (project scope, shared via version control):

{
  "mcpServers": {
    "karellen-rr-mcp": {
      "type": "stdio",
      "command": "karellen-rr-mcp"
    }
  }
}

If installed with pipx:

claude mcp add --transport stdio karellen-rr-mcp -- pipx run karellen-rr-mcp

or manually:

{
  "mcpServers": {
    "karellen-rr-mcp": {
      "type": "stdio",
      "command": "pipx",
      "args": ["run", "karellen-rr-mcp"]
    }
  }
}

Auto-approve rr tools

By default Claude Code will prompt for confirmation before each rr_* tool call. You can approve individually by selecting "Yes, and don't ask again" when prompted.

To auto-approve all tools upfront, add a permission rule to your user settings (~/.claude/settings.json):

{
  "permissions": {
    "allow": [
      "mcp__plugin_karellen-rr-mcp_karellen-rr-mcp__*",
      "mcp__karellen-rr-mcp__*"
    ]
  }
}

The first rule covers plugin-loaded tools, the second covers manual MCP configuration.

Or for a project-scoped setting, add the same rule to .claude/settings.json in your project root (this file can be committed to version control so all team members get it).

Available Tools

Session Lifecycle

Tool Description
rr_record Record a command with rr. Returns trace directory path.
rr_replay_start Start replay session (launches rr gdbserver + GDB/MI).
rr_replay_stop Stop current replay session, clean up.
rr_list_recordings List available rr trace recordings.
rr_ps List processes in a trace recording (PID, PPID, exit code, command).
rr_traceinfo Get trace metadata (header info in JSON format).
rr_rm Remove an rr trace recording.
rr_when Get current rr event number (position in trace).

Breakpoints

Tool Description
rr_breakpoint_set Set breakpoint at function/file:line/address.
rr_breakpoint_remove Remove a breakpoint.
rr_breakpoint_list List all breakpoints.
rr_watchpoint_set Set hardware watchpoint (write/read/access).

Execution Control

Tool Description
rr_continue Continue forward or backward.
rr_step Step into (forward or reverse).
rr_next Step over (forward or reverse).
rr_finish Run to function return (or call site if reverse).
rr_run_to_event Jump to specific rr event number.

Thread and Frame Navigation

Tool Description
rr_thread_list List all threads with state and location.
rr_thread_select Switch to a different thread.
rr_select_frame Select a stack frame for inspection (locals/evaluate use that frame).

State Inspection

Tool Description
rr_backtrace Get call stack.
rr_evaluate Evaluate C/C++ expression in current context.
rr_locals List local variables with values.
rr_read_memory Read raw memory bytes.
rr_registers Read CPU registers.
rr_source_lines List source code around current position.

Checkpoints

Tool Description
rr_checkpoint_save Save checkpoint at current position.
rr_checkpoint_restore Restore to saved checkpoint.

Configuration

Timeouts

All timeouts are configurable via environment variables (in seconds). Set them in your MCP server configuration:

{
  "mcpServers": {
    "karellen-rr-mcp": {
      "type": "stdio",
      "command": "karellen-rr-mcp",
      "env": {
        "RR_MCP_TIMEOUT_FORWARD": "300",
        "RR_MCP_TIMEOUT_REVERSE": "600"
      }
    }
  }
}
Variable Default Description
RR_MCP_TIMEOUT_STARTUP 30 Waiting for rr gdbserver to start listening
RR_MCP_TIMEOUT_CONNECT 60 GDB connecting to rr (includes symbol loading)
RR_MCP_TIMEOUT_FORWARD 120 Forward execution (continue, step, next, finish)
RR_MCP_TIMEOUT_REVERSE 300 Reverse execution
RR_MCP_TIMEOUT_BREAKPOINT 30 Breakpoint/watchpoint operations
RR_MCP_TIMEOUT_EVAL 30 State inspection (backtrace, evaluate, locals, etc.)

For large binaries (e.g. MariaDB, Firefox), you may need to increase RR_MCP_TIMEOUT_CONNECT (symbol loading can take 20+ seconds) and RR_MCP_TIMEOUT_FORWARD (replaying to a breakpoint deep in execution can take minutes).

Troubleshooting

AMD Zen CPUs

rr does not work reliably on AMD Zen CPUs unless the hardware SpecLockMap optimization is disabled. When running rr on Zen you may see:

On Zen CPUs, rr will not work reliably unless you disable the hardware SpecLockMap optimization.

Workaround: run the zen_workaround.py script from the rr source tree as root:

sudo python3 scripts/zen_workaround.py

This fix must be reapplied after each reboot or suspend. To make it persist, you must also stabilize the Speculative Store Bypass (SSB) mitigation by adding one of the following kernel command-line parameters:

  • spec_store_bypass_disable=on — fully enables SSB mitigation (has performance implications)
  • nospec_store_bypass_disable — fully disables SSB mitigation (has security implications)

Alternatively, build and load the zen_workaround.ko kernel module from the rr source tree, which prevents SSB mitigation from resetting the workaround without requiring kernel parameters.

See the rr Zen wiki page for full details.

MSR kernel module not loaded

The zen_workaround.py script accesses CPU model-specific registers via /dev/cpu/0/msr, which requires the msr kernel module. On many distributions this module is not loaded by default. If the script fails, load it manually:

sudo modprobe msr

To make this persistent across reboots:

echo 'msr' | sudo tee /etc/modules-load.d/msr.conf

Note: on systems with Secure Boot enabled, the msr module may fail to load because it is not signed. You may need to either disable Secure Boot in your UEFI/BIOS settings, or sign the module with your own Machine Owner Key (MOK).

MADV_GUARD_INSTALL crash on kernel 6.13+ with glibc 2.42+

Linux 6.13 introduced MADV_GUARD_INSTALL (madvise advice 102) for lightweight stack guard pages. glibc 2.42+ (e.g. Fedora 43) uses this in pthread_create. rr 5.9.0 (the latest release, from February 2025) does not recognize this madvise advice value and crashes with:

Assertion `t->regs().syscall_result_signed() == -syscall_state.expect_errno' failed to hold.
Expected EINVAL for 'madvise' but got result 0 (errno SUCCESS); unknown madvise(102)

This was fixed in rr git master (commit 34ff3a7, August 2025) but has not been included in a release yet. You must build rr from source to get the fix:

git clone https://github.com/rr-debugger/rr.git
cd rr
mkdir build && cd build
cmake ..
make -j$(nproc)
sudo make install

See rr-debugger/rr#4044 and rr-debugger/rr#3995 for details.

License

Apache-2.0

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured