tcai_meta_learning

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

With no annotations, the description carries the burden of behavioral disclosure. It explains the meaning of velocity>0 (converging) and noveltySpike (novel/confusing regime), and mentions optional RPE injection. However, it does not disclose side effects of injection, the return structure, or any prerequisites, leaving some behavioral ambiguity.

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 two sentences long, front-loaded with the main purpose, and every clause adds value (defining state variables and optional injection). It is concise and well-structured with no filler.

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?

In the absence of an output schema and annotations, the description does not specify the return format or fields such as velocity and noveltySpike, nor does it clarify what happens when an RPE is injected. For a state-reading tool, this leaves important contextual gaps, making it incomplete for an agent to fully anticipate tool behavior.

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 sole parameter 'rpe' is fully documented in the schema with type, range, and description (coverage 100%). The tool description adds little beyond this, only restating the optional injection. The schema already provides the necessary semantic meaning, so a baseline score of 3 is appropriate.

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 identifies a specific resource (MetaLearningModule port) and conveys the core function of reading meta-learning state (learning velocity, noveltySpike) with optional RPE injection. Although it lacks an explicit verb like 'get' or 'inject', the meaning is clear and it is distinguishable from sibling tools by its focus on RPE-variance dynamics.

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 description implies usage for checking convergence or novelty but does not explicitly state when to use this tool versus alternatives. No sibling tools such as tcai_convergence or tcai_metaconsciousness are mentioned, and there are no exclusion criteria or guiding conditions.

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