Get Glucose Summary

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

With no annotations provided, the description carries the burden of behavioral disclosure. It communicates the 24-hour window and the output metrics, which are useful, but it does not explicitly state that it is read-only or mention any permissions or side effect expectations. Non-destructive nature is implied but not stated.

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 a single, front-loaded sentence that efficiently lists the key output metrics and the time span. Every word contributes, with no filler or repetition.

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 tool is simple (3 optional parameters, no output schema), and the description fully explains the purpose, time window, and the expected return values (the listed metrics). This is sufficient for an agent to invoke the tool correctly.

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 input schema covers all parameters with high detail (patient_id, low_threshold, high_threshold, including defaults). The description adds no parameter-specific context beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 explicitly states the tool summarizes glucose trends over the past 24 hours, listing specific metrics (time-in-range, average, highs, lows, standard deviation). This clearly distinguishes it from siblings like get_current_glucose (single reading) and get_glucose_history (raw data).

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 implies the tool is for obtaining an aggregate summary rather than raw data, but it does not explicitly mention when to use it vs alternatives. No direct exclusions or alternative tool references are given, so it relies on implicit inference.

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