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

Annotations already mark it readOnly, idempotent, and non-destructive, so the description only needs to add context. It adds the important scale interpretation ('scaled against AI's own history since 2023, not a share of all jobs') and lists the exact returned fields, including the as-of date and uncertainty range, which help the agent understand the result without contradicting 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 two sentences, front-loads the tool's essential purpose and metric clarification, and then lists the output contents efficiently. The 'not a share of all jobs' caveat is placed early where it prevents misinterpretation, and every sentence adds value.

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?

Because there is no output schema, the description takes on the burden of describing the return value, and it does so thoroughly (value, band, uncertainty range, change, components, as-of date, citation string). With no parameters, read-only annotations, and low complexity, nothing essential is missing for an agent to call this tool correctly.

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 schema is empty and there are no parameter semantics to document. The description explicitly confirms 'Takes no arguments,' which is more than enough; per the rubric, zero-parameter tools get a baseline of 4.

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 names a specific resource (the AI Layoffs Index) and a clear action (returns the current reading), and explains the metric's scale and scope ('0-100 score... not a share of all jobs'). This clearly differentiates it from sibling tools that count or search individual layoff events/company layoffs.

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?

No guidance is given about when to prefer this tool over its siblings (count_ai_job_losses, get_company_ai_layoffs, search_ai_layoff_events). The phrase 'Current reading' implies the tool is for the aggregate index snapshot, but there are no explicit when-to-use or when-not-to-use instructions.

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