tcai_metaconsciousness

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

With no annotations, the description itself carries the transparency burden. It discloses that the result is a proxy rather than a measurement and lists the component dimensions, which adds meaningful behavioral context. It does not mention output format or side effects, but the zero-parameter nature strongly suggests a read-only computation.

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

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

The description is a single, well-structured sentence that front-loads the tool's identity and immediately provides the key caveat ('proxy, not measurement'). Every phrase earns its place with no redundancy.

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

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

For a zero-parameter tool with no output schema, the description covers the core purpose and a critical interpretive caveat. However, it lacks return scale/format details and explicit guidance on when to use this tool versus sibling meta-cognitive evaluators, leaving some ambiguity for an agent.

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

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

The tool has zero parameters and schema coverage is 100%, so no parameter explanation is needed. The description adds value by explaining what the composite score is based on, exceeding the empty schema's information.

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

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

The description clearly states the tool computes a weighted score over confidence calibration, learning awareness, self-continuity, and error monitoring. The 'PROXY of meta-representation capacity, not a measurement' framing distinguishes it as an evaluator rather than a direct measurement, separating it from sibling meta-cognitive tools.

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

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

The 'not a measurement' caveat implies the tool should be used as an approximate proxy, but there is no explicit when-to-use guidance or comparison with sibling tools like tcai_second_order or tcai_meta_learning. The intended context is only weakly implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.