get_glucose_stats

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

No annotations are provided, so the description carries the full burden. It discloses the calculation nature but doesn't mention any potential caveats, preconditions (e.g., connection), output format, or error handling. The description adds no behavioral context beyond the obvious read-only 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 concise sentences, first is a clear action statement, second adds context. No filler, front-loaded with the verb.

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 one optional param, no output schema, no annotations. The description lists the metrics computed, but misses any note on output structure or preconditions. It's adequate for a simple stats tool but leaves some gaps.

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 fully describes the days parameter with default and range. The description doesn't mention parameters but that's unnecessary given 100% coverage. Baseline 3 applies.

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?

Description clearly states the tool calculates glucose statistics with specific metrics (average, GMI, TIR, variability). It distinguishes from sibling tools 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 says it's 'essential for diabetes management insights' but doesn't explicitly contrast with sibling tools or state when to use it instead of history/trends. Usage is implied by the nature of the tool, but no direct alternatives are mentioned.

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