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tcai_metaconsciousness

Evaluates meta-consciousness by scoring confidence calibration, learning awareness, self-continuity, and error monitoring. Provides a proxy for meta-representation capacity.

Instructions

Meta-consciousness composite (MetaconsciousnessEvaluator port): weighted score over confidence calibration, learning awareness, self-continuity and error monitoring. PROXY of meta-representation capacity, not a measurement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description carries full burden. It mentions it's a proxy, not a measurement, but does not disclose whether it is read-only or has side effects. No information about permissions or data persistence.

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?

Two sentences conveying purpose, components, and caveat. No extraneous content, front-loaded with key information.

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?

Lacks details on return format (e.g., score range, type) and usage context. Since there is no output schema, the description should clarify the output, but it does not.

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?

Input schema has zero parameters, and schema coverage is 100%. Per guidelines, baseline is 4; description adds no parameter info because 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?

Description clearly states it provides a weighted score over four specific components (confidence calibration, learning awareness, self-continuity, error monitoring) and identifies itself as a proxy. This differentiates it from other tcai_* tools.

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 guidance on when to use this tool versus alternatives like tcai_self_model or tcai_second_order. The description does not specify prerequisites or context.

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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