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

Annotations already declare read-only, idempotent, closed-world, and non-destructive behavior, so safety is covered. The description adds genuine behavioral context beyond that: it decodes the status vocabulary (Clear/Inactive, Watch, Stress, Activated/Danger, Calibrating, Unknown), which an agent cannot learn from the schema or 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?

It leads with what is returned, then the motivating use case, then the status legend. Every sentence carries load — the legend is the only verbose part and it substitutes for a missing enum definition in the schema.

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, the description carries the burden of describing the return shape, and it does so well (indicators, status, layer, weight, explanation). The status legend further compensates for the schema's lack of enums, leaving little an agent needs missing.

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 takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate. The absence of a date/scope parameter is worth noting but does not lower the score.

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 states precisely what is returned: the ten indicators behind today's Zensei Index, each with status, scoring layer, weight, and the site-facing explanation. It is specific and distinguishable from siblings like zensei_index_summary or zensei_index_today, though it never explicitly contrasts itself with them.

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?

'Use it to explain why the index sits where it does' gives a clear selection rationale for drill-down diagnostics versus a top-level summary. It stops short of naming which sibling to prefer for aggregate views or index metadata, so no explicit alternatives or exclusions are stated.

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