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batteryshark

System Information MCP Server

by batteryshark

get_system_summary

Retrieve essential system information including hostname, OS, CPU, RAM, and uptime for basic system verification and health checks.

Instructions

Get essential system overview - hostname, OS, CPU, RAM, uptime.

Quick system identity check without heavy data collection. Perfect for basic system verification and health checks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The primary handler function for the 'get_system_summary' tool. Decorated with @mcp.tool for automatic registration in the FastMCP server. Collects essential system information using helper functions get_system_identity() and get_hardware_info(), filters key hardware metrics, formats output as markdown sections with timestamp, handles exceptions, and returns a ToolResult with text content.
    @mcp.tool
    def get_system_summary() -> ToolResult:
        """Get essential system overview - hostname, OS, CPU, RAM, uptime.
        
        Quick system identity check without heavy data collection.
        Perfect for basic system verification and health checks.
        """
        info_sections = []
        info_sections.append("# System Summary")
        info_sections.append(f"*Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*\n")
        
        try:
            # Just the essentials from system identity and hardware
            identity_info = get_system_identity()
            hardware_info = get_hardware_info()
            
            # Extract key lines only
            info_sections.extend(identity_info)
            
            # Add just CPU, RAM, and uptime from hardware
            for line in hardware_info:
                if any(keyword in line for keyword in ['CPU Cores:', 'CPU Usage:', 'Total RAM:', 'Available RAM:', 'Boot Time:', 'Uptime:']):
                    info_sections.append(line)
                    
        except Exception as e:
            info_sections.append(f"⚠️ **Error**: {str(e)}")
        
        return text_response("\n".join(info_sections))
Behavior3/5

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 discloses that this is a read operation ('Get') and emphasizes it's 'without heavy data collection,' which implies low resource usage and non-destructive behavior. However, it lacks details on potential rate limits, error conditions, or specific output format, leaving some behavioral aspects unclear.

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 highly concise and well-structured: the first sentence states the purpose with key details, and the next two sentences provide usage context. Every sentence adds value—none are redundant or vague—making it efficient and front-loaded for quick understanding by an AI agent.

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 tool's simplicity (0 parameters, no output schema, no annotations), the description is largely complete. It clearly explains what the tool does and when to use it, aligning with the sibling context. However, the lack of output schema means the description doesn't specify the return format (e.g., structured data vs. raw text), which is a minor gap for full contextual understanding.

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, with 100% schema description coverage (since there are no parameters to describe). The description does not need to add parameter semantics, and it appropriately avoids discussing inputs. A baseline of 4 is applied as per the rules for zero-parameter tools, as it correctly focuses on the tool's purpose without unnecessary parameter details.

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 explicitly states the tool's purpose with specific details: 'Get essential system overview - hostname, OS, CPU, RAM, uptime.' This clearly distinguishes it from sibling tools like get_full_system_report or get_hardware_details by emphasizing it's a 'quick system identity check without heavy data collection' for 'basic system verification and health checks.'

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool: 'Perfect for basic system verification and health checks.' It implicitly contrasts with siblings by noting it's 'without heavy data collection,' suggesting alternatives like get_full_system_report for more detailed analysis. This helps the agent choose appropriately based on the need for a lightweight overview versus comprehensive data.

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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