Skip to main content
Glama

debugpy_context

Attach debugpy to Python processes in Docker containers for debugging and inspection. Enables process injection and breakpoint planning based on logs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
containerYes
portNo
python_binNopython

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'debugpy_context' tool. It gathers comprehensive context about a container including process information, debugpy status, Python version, working directory, port mappings, and environment variables. The function is decorated with @mcp.tool() which registers it as an MCP tool.
    @mcp.tool()
    def debugpy_context(container: str, port: int = DEFAULT_PORT, python_bin: str = "python") -> dict[str, Any]:
        if not docker_inspect_running(container):
            return DebugContextResult(ok=False, container=container, notes=["Container is not running or does not exist."]).model_dump()
        installed, version = detect_debugpy_installed(container, python_bin=python_bin)
        processes = get_process_table(container)
        suggested_pid, pid_notes = choose_pid(processes)
        working_dir = get_working_dir(container, suggested_pid) if suggested_pid else None
        listening = port_is_listening(container, port)
        mapped_port = docker_port_mapping(container, port)
        py_ver = get_python_version(container, python_bin=python_bin)
        ports_snapshot = capture_ports_snapshot(container)
        env_subset = get_env_subset(container)
        notes = list(pid_notes)
        if not installed:
            notes.append("debugpy is not currently installed inside the container.")
        notes.append(f"Port {port} is {'already' if listening else 'not'} listening inside the container.")
        return DebugContextResult(ok=True, container=container, working_dir=working_dir, python_version=py_ver, debugpy_version=version, debugpy_listening=listening, mapped_port=mapped_port, processes=processes, suggested_pid=suggested_pid, ports_snapshot=ports_snapshot, env_subset=env_subset, notes=notes).model_dump()
  • The DebugContextResult Pydantic model that defines the output schema for the debugpy_context tool. It includes fields for container info, working directory, Python/debugpy versions, listening status, port mappings, process list, and diagnostic notes.
    class DebugContextResult(BaseModel):
        ok: bool
        container: str
        working_dir: str | None = None
        python_version: str | None = None
        debugpy_version: str | None = None
        debugpy_listening: bool = False
        mapped_port: str | None = None
        processes: list[ProcessInfo] = Field(default_factory=list)
        suggested_pid: int | None = None
        ports_snapshot: str | None = None
        env_subset: dict[str, str] = Field(default_factory=dict)
        notes: list[str] = Field(default_factory=list)
  • The @mcp.tool() decorator registers the debugpy_context function as an MCP tool, making it available for invocation by MCP clients.
    @mcp.tool()
  • Helper function get_process_table that retrieves the process table from a Docker container and classifies processes by type (uvicorn, gunicorn-master, gunicorn-worker, python, other). This is used by debugpy_context to identify candidate processes for debugging.
    def get_process_table(container: str) -> list[ProcessInfo]:
        proc = docker_exec(container, "ps -eo pid,ppid,args", timeout=20, check=False)
        if proc.returncode != 0:
            raise ToolError(f"Unable to read process table in container {container}:\n{proc.stderr}")
        results: list[ProcessInfo] = []
        for line in proc.stdout.splitlines()[1:]:
            line = line.strip()
            if not line:
                continue
            parts = line.split(None, 2)
            if len(parts) < 3:
                continue
            pid_s, ppid_s, cmd = parts
            try:
                pid = int(pid_s)
                ppid = int(ppid_s)
            except ValueError:
                continue
            lowered = cmd.lower()
            kind: Literal["uvicorn", "gunicorn-master", "gunicorn-worker", "python", "other"] = "other"
            if "gunicorn" in lowered and "master" in lowered:
                kind = "gunicorn-master"
            elif "gunicorn" in lowered and "worker" in lowered:
                kind = "gunicorn-worker"
            elif "uvicorn" in lowered:
                kind = "uvicorn"
            elif "python" in lowered:
                kind = "python"
            if any(token in lowered for token in ["python", "uvicorn", "gunicorn", "fastapi"]):
                results.append(ProcessInfo(pid=pid, ppid=ppid, cmd=cmd, kind=kind))
        return results
  • Helper function detect_debugpy_installed that checks if debugpy is installed in the container and retrieves its version. This is called by debugpy_context to determine if the debugger is available.
    def detect_debugpy_installed(container: str, python_bin: str = "python") -> tuple[bool, str | None]:
        cmd = (
            f"{shlex.quote(python_bin)} - <<'PY'\n"
            "import importlib.util\n"
            "spec = importlib.util.find_spec('debugpy')\n"
            "print('YES' if spec else 'NO')\n"
            "PY"
        )
        proc = docker_exec(container, cmd, timeout=20, check=False)
        if proc.returncode != 0:
            return False, None
        installed = proc.stdout.strip() == "YES"
        if not installed:
            return False, None
        ver_cmd = (
            f"{shlex.quote(python_bin)} - <<'PY'\n"
            "import debugpy\n"
            "print(getattr(debugpy, '__version__', 'unknown'))\n"
            "PY"
        )
        ver_proc = docker_exec(container, ver_cmd, timeout=20, check=False)
        version = ver_proc.stdout.strip() if ver_proc.returncode == 0 else None
        return True, version
Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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

Completeness1/5

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

Tool has no description.

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

Parameters1/5

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

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/will-garrett/debugpy-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server