tcai_cycle

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

No annotations are provided, so the description bears full burden. It describes the processing pipeline and the stopp condition, but lacks details on side effects (e.g., state mutation, data persistence, required permissions). Moderate disclosure.

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 four sentences, front-loading the purpose and key parameter behavior. Every sentence adds value, though it could be slightly more terse. No waste.

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?

With 15 parameters, no output schema, and no annotations, the description should provide more context—e.g., return value, how cycles parameter interacts with stopp thresholds, or typical usage patterns. Adequate but misses significant details.

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 80%, so baseline is 3. The description adds context for stopWhenSatisfied and mentions it can halt early. Other parameters (threat, novelty, etc.) are not elaborated beyond the schema, so description adds limited additional meaning.

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 runs ACM cycles and enumerates the pipeline steps (SNN signals → AKOrN binding → ... → second-order loop). It distinguishes itself from sibling tools like tcai_emotion_appraise or tcai_self_model by being the overarching cycle runner.

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 mentions using stopWhenSatisfied to halt early, but does not explicitly guide when to use this tool versus individual sibling tools (e.g., when to run a full cycle vs. a single step). Usage context is implied but not stated.

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