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debugger_status

Check available debuggers to monitor program execution and analyze binary states during debugging sessions.

Instructions

Get status of available debuggers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:42-45 (handler)
    The debugger_status() function is the MCP tool handler that returns status of available debuggers. It's registered with the @mcp.tool() decorator and calls DebuggerFactory.list_debuggers() to get the formatted output.
    @mcp.tool()
    def debugger_status() -> str:
        """Get status of available debuggers."""
        return DebuggerFactory.list_debuggers()
  • The list_debuggers() static method formats a human-readable list of available debuggers by calling get_available_debuggers() and creating a formatted string with status indicators (✓/✗) for each debugger type.
    def list_debuggers() -> str:
        """Get a formatted list of available debuggers."""
        available = DebuggerFactory.get_available_debuggers()
        
        lines = ["Available debuggers:"]
        for debugger_type, info in available.items():
            status = "✓ Available" if info['available'] else "✗ Not available"
            lines.append(f"  • {debugger_type.upper()}: {info['name']} - {status}")
            if info['description']:
                lines.append(f"    {info['description']}")
        
        return "\n".join(lines)
  • The get_available_debuggers() static method checks which debuggers (GDB and LLDB) are available on the system by calling their respective is_available() methods and returns a dictionary with availability status, names, and descriptions.
    @staticmethod
    def get_available_debuggers() -> dict:
        """Get information about available debuggers."""
        return {
            'gdb': {
                'available': GDBSessionManager.is_available(),
                'name': 'GNU Debugger (GDB)',
                'description': 'Traditional Unix debugger'
            },
            'lldb': {
                'available': LLDBSessionManager.is_available(),
                'name': 'LLVM Debugger (LLDB)', 
                'description': 'Modern LLVM-based debugger (macOS native)'
            }
        }
  • The is_available() static method for GDB checks if GDB is installed by running 'gdb --version' subprocess command and returns True if the command succeeds (returncode == 0).
    @staticmethod
    def is_available() -> bool:
        """Check if GDB is available on this system."""
        import subprocess
        try:
            result = subprocess.run(['gdb', '--version'], capture_output=True, timeout=5)
            return result.returncode == 0
        except (FileNotFoundError, subprocess.TimeoutExpired):
            return False
  • The is_available() static method for LLDB returns the LLDB_AVAILABLE global flag, which is set during module initialization when the LLDB Python module is successfully imported.
    @staticmethod
    def is_available() -> bool:
        """Check if LLDB is available on this system."""
        return LLDB_AVAILABLE
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states the tool gets status but doesn't disclose behavioral traits such as what 'status' includes (e.g., active sessions, debugger types), whether it's read-only or has side effects, or any rate limits. This leaves significant gaps in understanding how the tool behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It's front-loaded and directly states the tool's purpose, making it easy to parse and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally adequate. However, with no annotations and multiple sibling tools, it lacks context on what 'status' entails and when to use it, leaving room for improvement in guiding the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter information is needed. The description doesn't add parameter details, but this is appropriate given the lack of parameters, meeting the baseline for such cases.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get status') and target ('available debuggers'), making the purpose understandable. However, it doesn't differentiate from sibling tools like debugger_list_sessions or gdb_list_sessions, which might also provide status-like information about debuggers or sessions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. With multiple sibling tools like debugger_list_sessions and gdb_list_sessions that might overlap in functionality, the description lacks context on specific use cases, prerequisites, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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