LLDB-MCP Server Integration

What does “LLDB” MCP Server do?

LLDB-MCP is a tool that integrates the LLDB debugger with Claude’s Model Context Protocol (MCP). This integration allows AI assistants—such as Claude—to start, control, and interact with LLDB debugging sessions directly, enabling AI-assisted debugging workflows. With LLDB-MCP, developers can automate and streamline debugging tasks by leveraging natural language or LLM-driven interfaces to manage LLDB sessions, control program execution, inspect memory and variables, set breakpoints, and analyze stack traces. This significantly accelerates the debugging process, reduces manual intervention, and enables sophisticated, context-aware developer workflows.

List of Prompts

No explicit prompt templates are documented in the repository or README.

List of Resources

No explicit resources are documented in the repository or README.

List of Tools

The LLDB-MCP server exposes the following tools (as functions/commands) that can be used for interacting with LLDB:

• lldb_start: Start a new LLDB debugging session.
• lldb_terminate: Terminate an active LLDB session.
• lldb_list_sessions: List all currently active LLDB sessions.
• lldb_load: Load a program into LLDB for debugging.
• lldb_attach: Attach the debugger to a running process.
• lldb_load_core: Load a core dump file for post-mortem analysis.
• lldb_run: Run the loaded program.
• lldb_continue: Continue program execution after a breakpoint or stop.
• lldb_step: Step to the next line or instruction in the program.
• lldb_next: Step over function calls during debugging.
• lldb_finish: Execute until the current function returns.
• lldb_kill: Kill the running debugged process.
• lldb_set_breakpoint: Set a breakpoint at a specified location.
• lldb_breakpoint_list: List all currently set breakpoints.
• lldb_breakpoint_delete: Remove an existing breakpoint.
• lldb_watchpoint: Set a watchpoint on a variable or memory address.
• lldb_backtrace: Show the current call stack.
• lldb_print: Print the value of a variable or expression.
• lldb_examine: Examine memory at a specified address.
• lldb_info_registers: Display the values of CPU registers.
• lldb_frame_info: Get detailed information about a stack frame.
• lldb_disassemble: Disassemble machine code at a location.
• lldb_process_info: Get information about the current process.
• lldb_thread_list: List all threads in the current process.
• lldb_thread_select: Select a specific thread for inspection.
• lldb_command: Execute an arbitrary LLDB command.
• lldb_expression: Evaluate an expression in the current frame.
• lldb_help: Get help for LLDB commands.

Use Cases of this MCP Server

• AI-Assisted Debugging: Allow LLMs to directly control LLDB, automate session creation, breakpoints, and debugging commands, reducing manual intervention and speeding up bug resolution.
• Educational/Instructional Debugging: Enable step-by-step walkthroughs, explain stack traces, or demonstrate debugging techniques for students or new developers by automating LLDB tasks.
• Crash/Post-Mortem Analysis: Use LLDB-MCP to load and analyze core dumps, automate inspection of memory/registers, and facilitate root-cause analysis after program crashes.
• Continuous Integration Debug Automation: Integrate LLDB-MCP into CI pipelines to automatically run debugging scripts on failing test cases or crashes, collecting diagnostic information.
• Remote Debugging/Assistance: Allow remote AI agents or tools to attach to running processes, inspect program state, and assist in diagnosing problems without direct manual LLDB invocation.

How to set it up

Windsurf

1. Ensure you have Python 3.7+ and LLDB installed.
2. Clone the repository:

   git clone https://github.com/stass/lldb-mcp.git
   cd lldb-mcp
   

3. Install the required Python package:

   pip install mcp
   

4. Add the LLDB-MCP server to your Windsurf MCP configuration:

   "mcpServers": {
     "lldb-mcp": {
       "command": "python3",
       "args": ["/path/to/lldb-mcp/lldb_mcp.py"],
       "disabled": false
     }
   }
   

5. Save the configuration and restart Windsurf. Verify the LLDB-MCP server appears and is accessible.

Securing API Keys

If you need to secure API keys or sensitive environment variables, use the env property in your configuration:

"mcpServers": {
  "lldb-mcp": {
    "command": "python3",
    "args": ["/path/to/lldb-mcp/lldb_mcp.py"],
    "env": {
      "MY_SECRET_KEY": "env:MY_SECRET_KEY"
    },
    "inputs": {
      "api_key": "${MY_SECRET_KEY}"
    },
    "disabled": false
  }
}

Claude

1. Install Python 3.7+ and LLDB.
2. Clone and install as above.
3. Open Claude’s desktop app configuration.
4. Add the following to your MCP configuration:

   "mcpServers": {
     "lldb-mcp": {
       "command": "python3",
       "args": ["/path/to/lldb-mcp/lldb_mcp.py"],
       "disabled": false
     }
   }
   

5. Save and restart Claude. Verify the MCP server connection.

Cursor

1. Install dependencies (Python 3.7+, LLDB).
2. Clone the repo and install dependencies as above.
3. Edit Cursor’s MCP configuration file to include:

   "mcpServers": {
     "lldb-mcp": {
       "command": "python3",
       "args": ["/path/to/lldb-mcp/lldb_mcp.py"],
       "disabled": false
     }
   }
   

4. Save and restart Cursor.

Cline

1. Make sure Python 3.7+ and LLDB are installed.
2. Clone the repository and install the Python package as above.
3. Edit Cline’s configuration file:

   "mcpServers": {
     "lldb-mcp": {
       "command": "python3",
       "args": ["/path/to/lldb-mcp/lldb_mcp.py"],
       "disabled": false
     }
   }
   

4. Save and restart the Cline application.

Securing API Keys

Use the env and inputs fields as in the Windsurf example above for any sensitive credentials.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "lldb-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “lldb-mcp” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Our opinion

LLDB-MCP is a practical and focused MCP server for AI-assisted debugging. It excels at exposing LLDB’s functionality through MCP, but lacks advanced documentation for resources/prompts and doesn’t mention Roots or Sampling. It’s well-licensed and sees moderate community engagement. Overall, it’s a solid, specialized tool for developers needing automated debugging workflows.

MCP Score

Rating: 7/10 — LLDB-MCP is a robust, single-focus MCP server with clear utility for AI-driven debugging, but would benefit from richer resource/prompt documentation and explicit support for advanced MCP features.