get_statistics

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

With no annotations provided, the description carries the full disclosure burden and does convey that this is a non-mutating calculation tool that derives aggregate statistics from glucose data. It does not disclose how the hours window interacts with dateFrom/dateTo when both are supplied, how insufficient data is handled, or that the configurable targetLow/targetHigh parameters directly affect the TIR calculation.

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?

Two sentences with no filler: the first front-loads the verb and the complete list of computed outputs, and the second justifies the tool's value in assessing control quality. Every sentence earns its place.

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 has moderate complexity (five optional parameters with overlapping window semantics) and no output schema or annotations; the description covers expected outputs well by listing the statistics, and the schema covers defaults. But the parameter interplay question (hours vs. date range precedence) and the influence of configurable targets on TIR are left unaddressed, which is a meaningful gap for an agent choosing how to invoke it.

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%, so the schema already documents every parameter (hours, dateTo, dateFrom, targetLow, targetHigh) along with defaults and bounds. The description only implicitly references targets via 'time in various ranges' and adds no new parameter meaning beyond the schema baseline.

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 uses a specific verb and resource ('Calculate glucose statistics') and enumerates the exact statistics computed (TIR, average glucose, eA1c, SD, CV, time in ranges), making the tool's purpose unmistakable. This scope distinguishes it from siblings like get_current_glucose, get_glucose_history, and weekly_comparison, none of which compute aggregate 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 closing sentence ('Essential for understanding overall glucose control quality') implies this tool is for aggregate control assessment, giving the agent a usage context. However, it never states when not to use it or names alternatives such as weekly_comparison, compare_periods, or detect_patterns for other analysis needs, leaving routing decisions to inference.

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