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self_diagnose

Diagnose the adaptive learning system by running a full health check covering prevention rules, prompt intelligence, missed detections, tool usage, and autonomy rate to identify issues.

Instructions

TRIGGER: Call this for a full health check of the adaptive learning system. 🏥 Runs a complete diagnostic covering prevention rules, prompt intelligence, missed detections, tool usage, and autonomy rate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, and the description does not disclose whether the tool is read-only, has side effects, or requires permissions. For a tool that runs a diagnostic, behavioral impact details are missing.

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 concise with two sentences, uses an emoji for clarity, and front-loads the key trigger instruction. Every sentence adds value with no wasted words.

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 has no parameters and an output schema exists, the description adequately lists the areas covered by the diagnostic. It provides sufficient context for understanding the tool's scope.

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 zero parameters, so the baseline score is 4. The description adds no parameter information, which is acceptable since none are needed.

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 clearly states it performs a full health check of the adaptive learning system, covering specific areas like prevention rules and prompt intelligence. This distinguishes it from sibling diagnostic tools that focus on narrower aspects.

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?

The description indicates it should be called for a full health check (TRIGGER), but it does not provide guidance on when not to use it or offer explicit alternatives. Given the many sibling diagnostic tools, this is a limitation.

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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