get_long_term_trend

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

With no annotations, the description carries full behavioral burden. It discloses the tier-aware merging behavior and the recency gap closure, which is important for understanding the tool's output. It does not mention authorization or side effects, but those are likely minimal for a read operation. The description adds value beyond the bare schema.

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 (4 sentences), with the core purpose in the first sentence. Every sentence adds value: purpose, tier-aware behavior, use case, and parameter examples. No wasted words.

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?

The description covers the tool's complexity (merging logic, parameters, usage context). An output schema exists but is not shown; the description does not explain return values, which is acceptable given the schema. Overall, it provides sufficient context for correct tool invocation.

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 0%, so the description must compensate. It provides examples for metric_type (e.g., HKQuantityTypeIdentifierHeartRateVariabilitySDNN) and explains the months parameter with its default value (24). While it does not give constraints or a full list, the examples help the agent understand acceptable values.

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 provides 'Long-term trend for any metric' and explains the tier-aware merging of recent raw data with historical summaries, which distinguishes it from sibling tools like get_hrv_trend (specific to HRV). The verb 'get' and resource 'long-term trend' are specific and unambiguous.

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 says 'Best for multi-year / seasonal analysis,' which clearly conveys when to use it. It implies a long-term context but does not explicitly mention when not to use or name alternative tools. However, the context is sufficient for an AI agent to infer appropriate usage.

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