get_glucose_trends

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

With no annotations, the description carries the burden of behavioral disclosure. It explains the analytical purpose and the types of patterns examined, but does not explicitly state whether the operation is read-only, if it requires a connection or historical data, or what the output format is. The 'get' prefix in the name suggests safety, but the description itself does not confirm.

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 two sentences and front-loaded with the core action. The second sentence, while helpful, is somewhat generic ('Helps identify recurring patterns...') and could be more specific about output or use cases, but it does not contain unnecessary fluff.

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 absence of an output schema and annotations, the description should explain what the tool returns. It mentions identifying patterns but does not describe the structure of the result (e.g., a report, list, or chart). For a simple tool with one optional parameter, this is a moderate gap, making it only partially complete.

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 schema description covers 100% of the parameter, providing detailed semantics for the 'period' enum (daily, weekly, monthly) with usage guidance. The main description adds no additional parameter information, so it relies on the schema. The baseline score of 3 applies due to 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 specifies the tool's action ('Analyze') and resource ('glucose patterns'), naming specific pattern types (dawn phenomenon, meal responses, overnight stability). This distinguishes it from sibling tools like get_glucose_history (raw data) and get_glucose_stats (aggregated statistics).

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 conveys when to use the tool: 'Helps identify recurring patterns that may need attention or treatment adjustments.' It does not explicitly mention alternatives or exclusions, but the context is clear enough for an agent to select it for pattern analysis over raw history or basic stats.

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