state_layoff_totals

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

No annotations are provided, so the description carries the full burden. It discloses the key behavioral branching — output shape changes based on whether state is present — and notes the optional year restriction. It does not mention update cadence or empty-result behavior, but for a read-style aggregation tool these are minor gaps.

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 compact sentences, each carrying substantive information with no filler. The most important behavioral distinction is front-loaded and the optional parameter is stated last with minimal wording.

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 tool with two optional parameters, no output schema, and no annotations, the description is complete enough to invoke correctly: it states what is returned, how the two modes differ, and how the year parameter applies. No critical operational detail is 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema by explaining that state is the mode-switching parameter (absent = national leaderboard; present = per-state series) and that year is an optional restriction. This is useful behavioral context the schema alone does not provide.

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?

States a specific resource (state-level WARN layoff totals) and concrete behavior: monthly counts and workers affected, with a national leaderboard when no state is given and a monthly series when a state is given. This clearly distinguishes it from sibling tools like search_layoff_notices and latest_layoff_notices, which deal with individual notices rather than monthly aggregates.

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?

Gives clear context for the main usage decision: omit state for a national leaderboard, supply state for that state's month-by-month series, and optionally restrict to one year. It does not explicitly name sibling alternatives or exclusion conditions, but the conditional behavior is enough to guide correct use.

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