warn-act-layoffs

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 tool's non-data-returning behavior ('Returns no records'), mentions per-state freshness and coverage gaps, and notes the API key process. This gives an honest picture of an informational, read-only tool.

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 just two sentences, front-loads the most important access info ('Free, no API key required'), and packs the tool's scope into a compact list. Every phrase earns its place without padding.

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 informational tool with no output schema, this description is complete: it explains what the tool returns, what topics it covers, and where it fits in the workflow before search_layoff_notices. No missing critical context.

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 100% schema description coverage, so there are no parameters to explain. According to the baseline for zero-parameter tools, the description need not add parameter detail; the score of 4 reflects that.

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 a specific action ('Describes') and a specific resource ('US WARN Act layoff dataset'), then enumerates exactly what content is covered. It also distinguishes itself from search_layoff_notices by explicitly saying it 'Returns no records.'

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 gives direct sequencing guidance: 'Call this before search_layoff_notices to decide whether the data is useful.' It also signals no authentication prerequisite with 'Free, no API key required.' It does not explicitly discuss the sibling layoff_coverage_stats, but the context is clear enough.

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

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

With no annotations provided, the description carries the full behavioral burden. It discloses the API key requirement and the read-only nature of reporting coverage statistics. It stops short of describing output details or limitations, but for a zero-parameter query tool this is reasonable.

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 sentences, no filler. The first sentence states what the tool returns, and the second gives the use case and a key requirement. The description is well structured and easy to scan.

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 simple zero-parameter coverage query, the description covers the core purpose, use case, and authentication requirement. It lacks a schema or explicit output format, but the values the agent needs to see—state, count, and recent date—are clearly implied.

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?

There are zero parameters, so the baseline is 4. The description correctly avoids documenting nonexistent parameters and does not need to compensate for any schema gaps.

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 specifies exactly what the tool provides: per-state counts of notices and the most recent dates for the WARN corpus. It names the resource and output clearly, and the phrase 'check freshness before relying on a search result' differentiates it from the search_layoff_notices sibling.

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 explicitly states when to use this tool: before relying on a search result, to verify freshness. This gives clear contextual guidance, though it does not enumerate when-not-to-use or mention the describe_dataset sibling.

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

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

With no annotations present, the description carries the burden of behavioral disclosure. It states that the tool returns structured records and requires an API key. However, it does not disclose practical behaviors such as pagination behavior, maximum result limits, sort order, or how filters combine (AND/OR semantics), which are important for an effective search tool.

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 rich but compact, covering action, scope, return structure, use cases, and authorization in a single block. It is slightly expansive in the list of use cases, but each sentence contributes meaningful information and the core action is 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?

Without an output schema, the description appropriately explains the return format and acknowledgement of the API key requirement. It remains incomplete on pagination details, default behavior with no filters, and clarification of date bounds, but overall it provides enough context for an agent to begin using the 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 description maps natural languages like state, company, effective date range, and county to several parameters, which is helpful given 0% schema coverage. Still, it does not explain limit and offset, date formats, or how 'start_date' and 'end_date' interplay, so the meaning of two semantically significant parameters is left to the caller to infer.

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 uses a specific verb and resource ('Search US WARN Act mass-layoff and plant-closing notices'), enumerates search filters, and describes the structured return fields. It clearly distinguishes this tool from sibling tools like describe_dataset and layoff_coverage_stats by framing it as the search interface to WARN notice data.

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 provides strong contextual guidance with concrete use cases: layoff tracking, corporate distress, employment-law research, and recruiting/outplacement leads. It does not explicitly name sibling tools or state when not to use this tool, but the use-case framing implies appropriate scenarios and sets clear expectations.

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