tcai_meta_learning

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

Without annotations, the description must convey behavioral traits. It mentions optional RPE injection, suggesting potential state modification, but does not clarify if the tool is read-only or if injecting updates internal state. The output behavior is partially described but side effects are ambiguous.

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 extremely concise: three sentences, no filler, front-loaded with the main concept and key conditions.

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 description lacks an explicit mention of the return format or structure, despite having no output schema. The user must infer that velocity and noveltySpike are returned, but no details on data type or how to interpret them beyond the given conditions.

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% with a clear description for 'rpe'. The description adds only the phrase 'Optionally inject an RPE sample', which does not significantly enhance understanding beyond 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 clearly states the tool provides meta-learning state, specifically learning velocity and noveltySpike from RPE-variance dynamics. It distinguishes itself from siblings like tcai_curiosity by focusing on meta-learning state, but could be more explicit about the output format.

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?

No guidance is given on when to use this tool versus siblings such as tcai_second_order or tcai_calibrate. The description implies usage for monitoring meta-learning but lacks explicit contextual cues or alternatives.

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