Search WARN Act layoff notices by company name, city, or keyword.
Args:
query: Search term (company name, city, etc.)
state: Optional 2-letter state code to filter (e.g. CA, TX, NY)
limit: Max results to return (default 20, max 100)
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| state | No | ||
| api_key | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It mentions the limit cap (default 20, max 100) and that api_key enables higher rate limits, which is useful. However, it does not explain matching semantics (exact vs substring), whether results are sorted, or any authorization requirements beyond the optional api_key.
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 opens with a one-sentence summary followed by a concise, well-labeled argument list. Every line adds value with no redundancy, and the structure makes it easy to parse.
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?
The description covers all parameters and the core purpose, and an output schema exists so return values are handled. However, it does not mention how this tool relates to siblings or any potential limitations (e.g., data coverage, result ordering), leaving minor gaps for a search tool.
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 description coverage is 0%, and the description compensates fully by explaining each parameter: query (search term), state (optional filter with examples), limit (max results), and api_key (rate limits). This adds significant meaning beyond the raw schema.
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 the tool searches WARN Act layoff notices by company name, city, or keyword, with a specific verb and resource. This distinguishes it from sibling tools like get_company_layoffs (exact company lookup) and get_recent_layoffs (recency-based listing) by emphasizing free-form search.
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 implies usage for broad keyword searches but does not explicitly state when to prefer this over siblings such as get_company_layoffs or get_recent_layoffs. It gives context on parameters but no clear when-to-use / when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools overlap in function: get_recent_layoffs, get_state_summary, and search_layoffs all return layoff notices with various filters. get_state_intelligence also overlaps with get_state_summary but adds cross-dataset information. Descriptions help, but an agent may struggle to choose the right tool without careful reading.
Most tools follow a get_verb pattern and use snake_case consistently. A few deviations like authenticate (verb-only), search_layoffs, and export_records are action-oriented but acceptable, making the overall pattern mostly predictable.
With 12 tools, the server is well-scoped for a labor market intelligence API. Each tool serves a distinct purpose, and the count is neither sparse nor overwhelming for the domain.
The tool set covers core workflows: authentication, search, bulk export, state-level intelligence, risk signals, and talent pipeline. Minor gaps exist, such as no company-level cross-dataset view (similar to state_intelligence for a company) and no direct record-by-ID retrieval, but these are workable.