get_trends

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

With no annotations, the description carries the full burden. It notes the output contains deltas and direction, which is useful, but says nothing about permissions, whether it is read-only, rate limits, or how client_id scoping behaves. That is a large gap for a 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 one-line purpose statement is front-loaded and the Args block is compact, with no redundant prose. The 'Args:' framing is somewhat boilerplate but each line carries real content.

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?

An output schema exists so return values need not be re-explained, but the tool has five required parameters and three of them (client_id, start_date, end_date) are undocumented in both schema and description. Combined with absent usage guidance and no annotations, the definition is under-specified for the tool's complexity.

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 documents three of six parameters with concrete examples ('sleep'/'cardio', 'hrv'/'sleep_score') and explains granularity as bucket size, but leaves client_id, start_date, and end_date completely unexplained.

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 a specific resource ('time series with deltas/direction') and distinguishes itself as the 'generic' tool that works 'for any metric'. However, it never names the domain-specific siblings (get_sleep_data, get_cardio_metrics, etc.) that an agent must choose against, so the differentiation is only implicit.

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?

There is no explicit when-to-use guidance, no conditions, and no mention of alternatives. The word 'generic' hints that this is the catch-all path versus the domain-specific siblings, but that inference is left entirely to the agent.

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