Set learning enabled

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

No annotations are provided, so the description carries the full burden. It mentions modifying a 'local JSON store' but does not disclose behavioral traits such as persistence, side effects on existing learned styles, or any required permissions. The behavioral context is minimal.

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?

A single, concise sentence that efficiently communicates the tool's purpose with no redundant information.

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?

For a simple tool with one boolean parameter and no output schema, the description provides the core function. However, it lacks details on behavioral implications (e.g., impact on existing data, whether changes are reversible). Completeness is adequate but not exceptional.

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 parameter description is already clear. The tool description does not add new meaning beyond repeating what the schema says ('Set false to stop learning new style signals'). Baseline score applies.

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 verb 'Enable or disable' and the resource 'style learning in the local JSON store'. It distinguishes from sibling tools like 'observe_user_message' and 'distill_interaction_profile' by focusing specifically on toggling the learning setting.

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 usage for enabling or disabling learning but does not provide explicit guidance on when to use this tool versus alternatives like 'observe_user_message' or 'distill_interaction_profile'. No exclusions or when-not-to-use scenarios are mentioned.

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