polybugger-mcp 1.8.0

MCP server for multi-language debugging with AI agents via DAP (Python, Go, Rust, Node.js)

polybugger-mcp

Multi-language MCP debugger for AI agents. Debug Python, JavaScript/TypeScript, Go, and Rust with a single tool.

Supported Languages

Language	Debugger	Status
Python	debugpy (VS Code)	Stable
JavaScript/TypeScript	Node.js Debug Adapter	Stable
Go	Delve	Stable
Rust	CodeLLDB	Stable
C/C++	CodeLLDB	Stable

Demo

AI Debugging a Crashing Script

The AI uses polybugger to find a division-by-zero bug in a Python script:

Available Debug Tools

Session Continuity

Debug sessions persist across multiple interactions - no need to reconfigure breakpoints or restart:

Call Chain Visualization

See the complete call stack with source context at each frame:

Watch Expressions

Track variable values as they change through execution:

Why polybugger-mcp?

Feature	polybugger-mcp	Other MCP debuggers
Multi-Language	Python, JS/TS, Go, Rust, C/C++	Python only
Container Debugging	Docker, Podman, Kubernetes	Not available
Session Recovery	Resume debugging after server restart	Not available
Watch Expressions	Track values across debug steps	Planned for 2026
Pure Python	Single pip install, no Node.js	Requires Node.js runtime
HTTP API	Use independently of MCP	MCP-only

Key Features

• Multi-Language Debugging - Python, JavaScript/TypeScript, Go, Rust, and C/C++
• Container Debugging - Debug processes inside Docker, Podman, and Kubernetes
• Session Recovery - Persist debug state and resume after server restart
• Watch Expressions - Define expressions to track across every debug step
• Smart Data Inspection - Intelligent preview of DataFrames, NumPy arrays, dicts, and lists
• Call Hierarchy - Visualize the complete call chain with source context
• Full Interactive Debugging - Breakpoints, stepping, pause/continue
• Variable Inspection - View locals, globals, evaluate arbitrary expressions
• Rich TUI Output - ASCII box-drawn tables and diagrams for better visualization
• Pure Python - No Node.js required, just pip install
• Dual Interface - Use via MCP or standalone HTTP API
• Multi-Client Support - Cursor, VS Code, Claude Desktop, and more

Installation

Quick Install (no clone required)

Using uvx (recommended):

uvx polybugger-mcp

Using pipx:

pipx run polybugger-mcp

Using pip:

pip install polybugger-mcp
polybugger-mcp

MCP Client Configuration

Configure your MCP client to use one of these commands:

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "polybugger": {
      "command": "uvx",
      "args": ["polybugger-mcp"]
    }
  }
}

Alternative: using pip install

{
  "mcpServers": {
    "polybugger": {
      "command": "python",
      "args": ["-m", "polybugger_mcp.mcp_server"]
    }
  }
}

VS Code

Use the VS Code CLI:

code --add-mcp '{"name":"polybugger","command":"uvx","args":["polybugger-mcp"]}'

Or add to your MCP settings manually.

Claude Code

claude mcp add polybugger -- uvx polybugger-mcp

Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "polybugger": {
      "command": "uvx",
      "args": ["polybugger-mcp"]
    }
  }
}

OpenCode

Add to ~/.config/opencode/opencode.json:

{
  "mcp": {
    "polybugger": {
      "type": "local",
      "command": ["uvx", "polybugger-mcp"],
      "enabled": true
    }
  }
}

Windsurf

Add to your Windsurf MCP config:

{
  "mcpServers": {
    "polybugger": {
      "command": "uvx",
      "args": ["polybugger-mcp"]
    }
  }
}

Cline

Add to your cline_mcp_settings.json:

{
  "mcpServers": {
    "polybugger": {
      "command": "uvx",
      "args": ["polybugger-mcp"],
      "disabled": false
    }
  }
}

Goose

Go to Settings > Extensions > Add custom extension:

• Type: STDIO
• Command: uvx polybugger-mcp

Docker

docker run -i --rm ghcr.io/wilfoa/polybugger-mcp

Or in your MCP config:

{
  "mcpServers": {
    "polybugger": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "ghcr.io/wilfoa/polybugger-mcp"]
    }
  }
}

Available Tools (28 tools)

Session Management

Tool	Description
debug_create_session	Create a new debug session (supports language parameter)
debug_list_sessions	List all active debug sessions
debug_get_session	Get detailed session information
debug_terminate_session	End a debug session and clean up
debug_list_languages	List supported programming languages

Breakpoints

Tool	Description
debug_set_breakpoints	Set breakpoints in source files (with optional conditions)
debug_get_breakpoints	List all breakpoints for a session
debug_clear_breakpoints	Remove breakpoints from files

Execution Control

Tool	Description
debug_launch	Launch a program for debugging
debug_continue	Continue execution until next breakpoint
debug_step	Step execution: mode="over" (next line), "into" (enter function), "out" (exit function)
debug_pause	Pause a running program
debug_attach	Attach to a running debugpy server

Inspection

Tool	Description
debug_get_stacktrace	Get the current call stack (supports TUI format)
debug_get_scopes	Get variable scopes (locals, globals)
debug_get_variables	Get variables in a scope (supports TUI format)
debug_evaluate	Evaluate an expression in the current context
debug_inspect_variable	Smart inspection of DataFrames, arrays, dicts with metadata
debug_get_call_chain	Call hierarchy with source context for each frame

Watch Expressions

Tool	Description
debug_watch	Manage watches: action="add", "remove", or "list"
debug_evaluate_watches	Evaluate all watch expressions

Events & Output

Tool	Description
debug_poll_events	Poll for debug events (stopped, terminated, etc.)
debug_get_output	Get program stdout/stderr

Recovery

Tool	Description
debug_list_recoverable	List sessions that can be recovered
debug_recover_session	Recover a session from previous server run

Container Debugging

Tool	Description
debug_container_list_processes	List Python processes in a container
debug_container_attach	Attach to a process in a container (auto-injects debugpy)
debug_container_launch	Launch a program with debugging in a container

Quick Start

1. Install the package:

   pip install polybugger-mcp
   

2. Configure your MCP client (see Installation above)

3. Start debugging: Ask your AI assistant:

   "Debug my script.py - set a breakpoint on line 15 and show me the variables when it stops"

Example Workflows

Basic Debugging

You: Debug tests/test_example.py - I want to see why the calculate function returns wrong results

AI: I'll create a debug session and set breakpoints in the calculate function.
    [Creates session, sets breakpoints, launches program]

    The program stopped at line 23. Here are the local variables:
    - x = 10
    - y = 5
    - result = 50  # This should be 15!

    I see the issue - you're using multiplication instead of addition on line 24.

Data Science Debugging

You: Debug my data pipeline and inspect the DataFrame at line 45

AI: [Sets breakpoint, launches program]

    The DataFrame `df` has 1000 rows x 5 columns (78.1 KB):

    ┌────────────┬────────────────┬────────┐
    │ Name       │ Type           │ Nulls  │
    ├────────────┼────────────────┼────────┤
    │ id         │ int64          │ -      │
    │ name       │ object         │ 5      │
    │ value      │ float64        │ 12     │
    └────────────┴────────────────┴────────┘

    I found 17 null values. The `value` column has NaN entries that may cause issues.

Call Hierarchy Visualization

You: Show me how we got to this breakpoint

AI: [Uses debug_get_call_chain with format="tui"]

    CALL CHAIN (4 frames)
    ════════════════════════════════════════════════════════════

    main (app.py:50)
    │ >> 50 │     result = process_order(order)
    │
    └─▶ process_order (orders.py:76)
        │ >> 76 │     pricing = apply_pricing(items, tier)
        │
        └─▶ apply_pricing (pricing.py:58)
            │ >> 58 │     discount = calculate_discount(subtotal)
            │
            └─▶ calculate_discount (pricing.py:23)  ◀── YOU ARE HERE
                │ >> 23 │     return base * rate

Container Debugging

You: Debug the Python process running in my Docker container "api-server"

AI: I'll list the Python processes in that container and attach the debugger.
    [Lists processes, finds PID 1234, injects debugpy, attaches]

    Successfully attached to process 1234 in container "api-server".
    The program is paused at the entry point. Here's the current state:
    - File: /app/server.py
    - Function: handle_request
    - Line: 45

    I can see the request object contains invalid JSON. The 'data' field is None.

Multi-Language Debugging

You: Debug my Go server - it's crashing on startup

AI: I'll create a Go debug session using Delve.
    [Creates session with language="go", sets breakpoints, launches]

    The program stopped at main.go:23. The config variable is nil
    because the environment variable CONFIG_PATH is not set.

Configuration

Environment variables (prefix with PYBUGGER_MCP_):

Variable	Default	Description
HOST	127.0.0.1	Server bind address
PORT	5679	Server port (for HTTP mode)
MAX_SESSIONS	10	Maximum concurrent debug sessions
SESSION_TIMEOUT_SECONDS	3600	Session idle timeout (1 hour)
DATA_DIR	~/.polybugger-mcp	Data directory for persistence
LOG_LEVEL	INFO	Logging level

Development

# Clone and setup
git clone https://github.com/wilfoa/polybugger-mcp.git
cd polybugger-mcp
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

# Run tests
make test

# Run linter
make lint

# Run type checker
make typecheck

Recording Demo GIFs

Demo GIFs are created using VHS with OpenCode as the MCP client. To regenerate them:

# Install VHS
brew install charmbracelet/tap/vhs

# Configure OpenCode with polybugger MCP (in ~/.config/opencode/opencode.json)
# Add: "mcp": { "polybugger": { "type": "local", "command": ["uvx", "polybugger-mcp"], "enabled": true } }

# Generate the GIFs
vhs docs/tapes/debug_demo.tape              # Shows available debug tools
vhs docs/tapes/debug_full_demo.tape         # Full debugging workflow
vhs docs/tapes/session_continuity_demo.tape # Session persistence across interactions
vhs docs/tapes/call_chain_demo.tape         # Call stack visualization
vhs docs/tapes/watch_expressions_demo.tape  # Watch expressions feature
vhs docs/tapes/container_debug_demo.tape    # Container debugging

Architecture

                              ┌─────────────────┐
                              │  debugpy        │──▶ Python
                              ├─────────────────┤
AI Agent  ◀──▶  MCP Server  ◀─┤  Node Debug     │──▶ JavaScript/TypeScript
                              ├─────────────────┤
                              │  Delve          │──▶ Go
                              ├─────────────────┤
                              │  CodeLLDB       │──▶ Rust/C/C++
                              └─────────────────┘
                                     │
                              ┌──────┴──────┐
                              │  Container  │
                              │  Runtimes   │
                              ├─────────────┤
                              │ Docker      │
                              │ Podman      │
                              │ Kubernetes  │
                              └─────────────┘

The MCP server translates tool calls to Debug Adapter Protocol (DAP) messages for each language's debugger, enabling full debugging capabilities through natural language.

Requirements

• Python 3.10 or higher
• Works on macOS, Linux, and Windows

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.