get_fasting_stats

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

Annotations already declare the operation as read-only, idempotent, and non-destructive, so the description does not need to restate safety. The description adds meaningful behavioral context by specifying that the tool returns computed aggregates rather than raw entry data, which is not captured by the annotations.

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 extremely concise: one clear purpose sentence followed by a line enumerating return values. It is front-loaded with the core action and adds no filler or redundant information.

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?

For a zero-parameter, read-only aggregated stats tool with rich annotations and an output schema, the description covers the essential information. It clearly states what the tool does and what it returns, and there are no hidden inputs or side effects an agent would need to know about.

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 tool has zero parameters and schema description coverage is 100%, so there is no parameter semantics burden on the description. The baseline of 4 applies because with no parameters, nothing additional is needed.

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 states that the tool retrieves 'aggregate fasting statistics' and enumerates the exact computed metrics returned: total fasting hours, longest fast, average fast duration, and completed fast count. This distinguishes it from the sibling get_fasting_history, which implies raw history rather than summary metrics.

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 word 'aggregate' and the list of summary metrics imply this tool is for overview statistics rather than detailed history, but the description does not explicitly mention when to prefer it over get_fasting_history or other fasting-related tools. No alternatives or exclusion criteria are named.

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