tcai_meta_learning

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

With no annotations, the description discloses that velocity>0 indicates converging and noveltySpike indicates novel regime. However, it does not clarify if injecting an RPE sample mutates state, nor does it mention any side effects, authorization needs, or rate limits.

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, concise and front-loaded. However, the first sentence packs multiple concepts without clear separation, slightly reducing readability.

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?

Given no output schema, the description mentions velocity>0 and noveltySpike as outputs, but does not specify the return format or structure. For a simple read tool with one optional parameter, it is adequate but not fully complete.

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%, so baseline is 3. The description adds 'Optionally inject an RPE sample', which is already covered by the schema's parameter description. No additional meaning 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 states the tool reads meta-learning state (learning velocity, novelty spike) and optionally injects RPE. The verb is implied (read/query), and the resource is the MetaLearningModule port. It distinguishes from sibling tcai_* tools by focusing on learning velocity from 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?

No explicit guidance on when to use this tool versus alternatives like tcai_curiosity or tcai_convergence. The description does not mention context, prerequisites, or exclusions.

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