add_reminder

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions '즉시 추가합니다' (adds immediately), which implies a write operation, but does not disclose other critical traits such as authentication requirements, error handling, rate limits, or what happens on success/failure. The description adds minimal behavioral context beyond the basic action, which is insufficient for a mutation tool with zero annotation coverage.

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 concise and front-loaded, with the core purpose stated first ('Apple 미리 알림에 할 일을 즉시 추가합니다') followed by a usage instruction. Both sentences are relevant and earn their place, avoiding redundancy. However, it could be slightly more structured (e.g., separating purpose from guidelines more clearly), preventing a perfect score.

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 tool's complexity (a mutation tool with 4 parameters, no annotations, and no output schema), the description is incomplete. It lacks details on behavioral aspects (e.g., permissions, side effects), output format, error conditions, and does not fully compensate for the absence of structured metadata. This makes it inadequate for safe and effective use by an AI agent.

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 description coverage is 100%, with all parameters well-documented in the input schema (e.g., title, due_date format, list default). The description adds marginal value by reinforcing that natural language input should be mapped to these parameters, but does not provide additional semantic details beyond what the schema already covers. This meets the baseline for high schema coverage.

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: 'Apple 미리 알림(Reminders)에 할 일을 즉시 추가합니다' (Adds a task immediately to Apple Reminders). It specifies the verb ('추가합니다' - adds) and resource ('Apple 미리 알림' - Apple Reminders), making the action explicit. However, it lacks differentiation from sibling tools (none provided), which prevents a perfect score.

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 some usage context by instructing to '자연어로 입력받은 내용을 title/due_date/list/notes로 정리해서 호출하세요' (call by organizing naturally entered content into title/due_date/list/notes), implying that natural language input should be parsed into structured parameters. However, it does not explicitly state when to use this tool versus alternatives (no siblings listed) or any prerequisites/exclusions, leaving usage guidance incomplete.

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