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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds meaningful interpretive context — that each event includes date, approximate headcount, and source, and that these are not a count of all job cuts — which prevents misuse. It stops short of describing pagination or retrieval behavior.

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 tight sentences with zero waste. The core definition comes first, followed immediately by the crucial scope caveat, keeping the most decision-relevant information front-loaded.

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 one-parameter, read-only list tool with no output schema, the description is complete: it explains what is returned (date, approximate headcount, source), what is excluded (all-cause layoffs), and the curated source. An agent has everything needed to call it 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?

Schema coverage is 100%, and the single optional limit parameter is fully documented in the schema with its default and bounds. The description adds no parameter-specific meaning beyond the schema, so the 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?

The description states a specific resource (recent layoffs where the company cited AI), names the data source (AI-Safe Careers' curated tracker), and explicitly distinguishes it from all-cause layoff datasets. Sibling tools (get_ai_exposure, search_occupations) cover different domains, so the scope is clearly separable.

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 makes clear when this tool applies: only company-cited AI layoffs, with all-cause layoffs tracked separately and never included. It does not name sibling tools or give explicit when-not-to-use guidance beyond excluding all-cause datasets, so it falls short of a 5.

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