Observe user message

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

No annotations are provided, so the description must carry behavioral disclosure. It states 'Learn' which implies a side effect (updating style memory), but it does not explicitly confirm persistence, return behavior, or whether it is idempotent. This leaves ambiguity for the agent.

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 core purpose, and contains no extraneous information. Every phrase earns its place, making it efficient and easy to parse.

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 the lack of output schema and no annotations, the description could be more complete by explicitly stating whether the tool stores observations or returns a result. The mention of 'three repetitions' for stable habits hints at storage, but it is not confirmed, leaving a gap in completeness.

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%, but the description adds valuable context beyond what the schema provides: emphasizing security (not to pass secrets) and explaining the purpose of hints with examples (e.g., 'self-invented sentence-final particle'). This clarifies parameter usage meaningfully.

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's purpose: learning lightweight conversational style signals from the latest user message. It uses a specific verb 'Learn' and resource 'conversational style signals', and conceptually distinguishes itself from sibling tools focused on retrieval, forgetting, or pinning.

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 provides guidance on what to pass (only message text, not secrets or logs) and hints about optional usage. However, it does not explicitly contrast when to use this tool versus alternatives like distill_recent_style or review_style_habits, limiting its utility for tool selection.

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