get_glucose_stats

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

Annotations only supply readOnlyHint=true, and the description adds meaningful behavioral context: the LibreLinkUp ~12-hour data availability limit and the fact that response data_coverage will usually be shorter than requested. Useful, though no rate-limit or error behavior is disclosed.

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?

Three sentences, front-loaded with the deliverable and followed by the practical caveat. The closing "Essential for diabetes management insights" is slightly promotional filler, but nothing is wasted.

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?

With no output schema and one optional parameter, the description does the needed work by naming the returned statistics and the data_coverage caveat. It is essentially complete for this 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 100% for the single 'days' parameter (range 1-14, default 7, availability caveat), so the schema already carries the semantics. The description adds no further parameter detail, making the baseline 3 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?

Specific verb+resource ("Calculate comprehensive glucose statistics") with an explicit enumeration of outputs (average glucose, GMI, time-in-range, variability), which clearly separates it from siblings like get_glucose_trends and get_glucose_history.

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?

States the general value ("diabetes management insights") and warns about the 12-hour data window, but gives no explicit routing to alternatives such as get_glucose_trends or get_glucose_history, leaving the when-to-use choice implied.

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