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carlosedp

Windows MCP Server

by carlosedp

Windows-gpu-info

Retrieve GPU information from Windows system. Get details about your graphics processing unit for diagnostics and monitoring.

Instructions

Get GPU information of the Windows system.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:193-214 (handler)
    The handler function for the 'Windows-gpu-info' tool. It runs a PowerShell command to get GPU info via Win32_VideoController, parses JSON output, and returns GPU name + driver version.
    def get_gpu_info() -> str:
        """Get GPU information of the Windows system."""
        import json
    
        try:
            output = utils._run_powershell(
                "Get-CimInstance -Class Win32_VideoController | Select-Object Name, DriverVersion | ConvertTo-Json -Depth 3"
            ).strip()
            if not output:
                return "No GPU information found."
            data = json.loads(output)
            if isinstance(data, dict):
                data = [data]
            gpu_info = []
            for item in data:
                name = item.get("Name")
                driver_version = item.get("DriverVersion")
                if name and driver_version:
                    gpu_info.append(f"GPU: {name}, Driver Version: {driver_version}")
            return "\n".join(gpu_info) if gpu_info else "No GPU information found."
        except Exception as e:
            return f"Error retrieving GPU info: {e}"
  • main.py:189-192 (registration)
    The MCP tool registration decorator that registers 'Windows-gpu-info' with a description and binds it to the get_gpu_info handler function.
    @mcp.tool(
            name="Windows-gpu-info",
            description="Get GPU information of the Windows system."
    )
  • Helper utility '_run_powershell' used by the GPU tool to execute a PowerShell command and return its stdout.
    def _run_powershell(command: str, timeout: int = 10) -> str:
        """Run a PowerShell command using pwsh if available, else Windows PowerShell."""
        import shutil as _shutil
        import subprocess
    
        shell = _shutil.which("pwsh") or "powershell"
        result = subprocess.run(
            [shell, "-NoProfile", "-Command", command],
            capture_output=True,
            text=True,
            timeout=timeout,
            check=True,
        )
        return result.stdout
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It only states 'Get GPU information' implying a read operation, but does not clarify whether it requires admin rights, if it is safe, or any potential side effects. The minimal text fails to provide additional behavioral context.

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, clear sentence with no wasted words. It is appropriately sized and front-loaded, conveying the essential purpose efficiently.

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

Completeness4/5

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

Given the simplicity (no parameters, output schema exists), the description is mostly adequate. It explains the tool's purpose, and the output schema handles the return details. However, a slightly richer description mentioning specific GPU attributes (e.g., name, driver) could improve completeness.

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?

There are no parameters; per guidelines, the baseline for 0 parameters is 4. The description does not need to explain parameters since none exist.

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

Purpose5/5

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

The description 'Get GPU information of the Windows system' clearly specifies the verb (Get), resource (GPU information), and scope (Windows system). It distinguishes well from sibling tools like Windows-cpu-info or Windows-memory-info, which target different system resources.

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

Usage Guidelines3/5

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

No explicit guidance on when to use or not use this tool is provided. The usage is implied: use for GPU information. However, there is no mention of alternative tools or exclusions, so it scores at the implied-usage level.

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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