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dnnyngyen
by dnnyngyen

HealthCheck

Assess system cognitive readiness and detect thinking bottlenecks. Evaluate if your infrastructure is prepared for intelligent decision-making beyond basic functionality.

Instructions

System intelligence assessment - evaluates not just operational health, but the cognitive readiness of your tools and infrastructure. When questioning system performance, ask: Are your tools thinking clearly? What might cognitive degradation look like in an AI system? This tool prompts you to consider: How do I assess whether my system is ready for intelligent decision-making, not just basic functionality? What early warning signs might indicate declining analytical capability?

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailedNoDiagnostic depth preference - How much system introspection do you need to assess cognitive readiness and identify potential thinking bottlenecks?
Behavior2/5

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

No annotations provided, so description must disclose behavioral traits. It fails to describe what the tool actually does—e.g., what endpoints it hits, what checks it performs, or its safety profile. Metaphorical language ('thinking clearly') does not convey concrete behavior.

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

Conciseness2/5

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

The description is overly verbose and uses rhetorical questions ('What might cognitive degradation look like?') that waste space. A concise description would state the tool's function directly.

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

Completeness2/5

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

No output schema exists, so description should explain return value or side effects. It does not. The tool has only one optional parameter, but the description leaves the agent without sufficient information to predict the tool's behavior.

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

Parameters3/5

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

The single 'detailed' parameter has a description that adds some meaning ('Diagnostic depth preference'), but it remains abstract. Schema coverage is 100%, so baseline is 3; the description provides marginal extra value.

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

Purpose2/5

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

The description uses vague terms like 'cognitive readiness' and mixes operational health with intelligence assessment, failing to state a clear verb+resource. It does not distinguish from sibling tools like JARVIS or PythonComputationalTool.

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?

No guidelines on when to use this tool versus alternatives. The description lacks any context on prerequisites or scenarios where this tool is appropriate.

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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