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check_system_status

Monitor the health and configuration status of the thinking augmentation system to verify API availability and operational readiness.

Instructions

Check the status of the thinking augmentation system.

Returns: Dictionary containing system status information including API key availability, model configurations, and health status

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'check_system_status' tool. It is decorated with @app.tool() for registration and implements the logic to check API key status, system health, and return configuration details.
    @app.tool()
    async def check_system_status() -> dict:
        """
        Check the status of the thinking augmentation system.
    
        Returns:
            Dictionary containing system status information including
            API key availability, model configurations, and health status
        """
        try:
            # Check API key from environment
            import os
    
            api_key = os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")
            if api_key:
                api_key_status = "configured"
                api_key_masked = (
                    f"{api_key[:8]}...{api_key[-4:]}" if len(api_key) > 12 else "***"
                )
            else:
                api_key_status = "missing"
                api_key_masked = "not_configured"
    
            # System is functional (no global state)
            system_initialized = True
    
            status = {
                "system_status": "healthy"
                if api_key_status == "configured"
                else "unhealthy",
                "api_key_status": api_key_status,
                "api_key_masked": api_key_masked,
                "system_initialized": system_initialized,
                "models": {
                    "critique_model": "gemini-2.5-flash",
                    "synthesis_model": "gemini-2.5-pro",
                },
                "capabilities": [
                    "parallel_critique_analysis",
                    "structured_synthesis",
                    "markdown_proposal_processing",
                ],
            }
    
            logger.info("System status check completed")
            return status
    
        except Exception as e:
            logger.error(f"Error checking system status: {e}")
            return {"system_status": "error", "error": str(e), "system_initialized": False}
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 the tool returns a dictionary with specific status information (API key availability, model configurations, health status), which adds useful context beyond a basic read operation. However, it doesn't cover other behavioral aspects like error handling, performance, or prerequisites, leaving gaps.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, with the core purpose stated first and return details following. It uses two sentences efficiently, though the second sentence could be slightly more concise (e.g., by integrating the return details into the first sentence).

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's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It explains the return content well, but without annotations or output schema, it lacks details on format, error cases, or integration with the sibling tool, making it minimally viable.

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 appropriately doesn't discuss parameters, earning a baseline score of 4 for not adding unnecessary details.

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 tool's purpose as checking the status of a specific system ('thinking augmentation system'), which is a specific verb+resource combination. However, it doesn't explicitly differentiate from the sibling tool 'consult_the_council', which might be related but isn't described here, preventing a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'consult_the_council' or any other context for usage decisions, leaving the agent with no explicit or implied usage instructions.

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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