learn

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

With no annotations, the description bears full responsibility for behavioral disclosure. It only lists action names without explaining side effects, permissions, or state changes. For example, 'record_missed_detection' might require specific auth or have data impacts, but no details are given. Minimal transparency.

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 relatively concise with a short paragraph and list. However, it lacks front-loading of key purpose and the action list is not structured hierarchically. The first sentence is vague ('Adaptive learning: prevention rules...') rather than stating the tool's primary function directly.

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 complexity (multiple actions, many sibling tools) and the lack of annotations, the description is incomplete. It does not explain return values (though an output schema exists), prerequisites, or the relationship to atomic tools. The agent would need additional context to use it effectively.

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?

The description adds brief semantics for each parameter: action is 'Which learning operation', subject is 'What was missed or what rule to create', context is 'Action-specific parameters'. This goes beyond the bare schema field names, but the descriptions are generic and lack specifics for each action. Adequate but not rich.

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 is for 'Adaptive learning: prevention rules, missed detections, team sync' and lists actions. This clarifies the domain and operations, but fails to differentiate from sibling tools that correspond to the exact same actions (e.g., record_missed_detection, register_prevention_rule). The purpose is clear but not distinctive.

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 provided on when to use this tool versus the individual sibling tools that perform each action. The description does not mention any context, prerequisites, or alternatives, leaving the agent without decision-making support.

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