tcai_meta_learning

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

With no annotations, the description carries the full burden for transparency. It mentions 'Optionally inject an RPE sample' but does not disclose whether this mutates internal state, whether the operation is reversible, or any side effects. Partial credit is given for the state interpretation, but critical behavioral 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.

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

The description is compact at two sentences and front-loads the core purpose. Technical jargon ('RPE-variance dynamics') reduces readability, but there is no fluff and every clause contributes meaning.

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?

The tool has no output schema and no annotations, so the description must explain return values and side effects. It does not specify what exactly is returned (e.g., a status object, both velocity and noveltySpike), nor the effect of an injected RPE sample on the returned state. This is a significant gap for a dual read/inject tool.

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?

Schema coverage is 100% and the 'rpe' parameter is already described in the schema with range constraints. The description's mention of 'inject an RPE sample' adds no new detail, so it does not improve on the schema.

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 its key outputs ('learning velocity', 'noveltySpike'), which distinguishes it from sibling tools like tcai_curiosity or tcai_convergence. However, it lacks an explicit verb (e.g., 'get', 'inject'), so the primary action is ambiguous.

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 when to use it (inspecting meta-learning state) and gives interpretation of state values ('velocity>0 ⇒ converging; noveltySpike ⇒ novel/confusing regime'), but it does not explicitly state when to use this over alternatives or mention any exclusions.

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