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?
No annotations are provided, so the description carries the transparency burden. It usefully discloses that an API key can raise rate limits and that limit has default/max bounds. However, it does not mention whether results are paginated, whether authentication is required, or any side effects (though 'search' implies non-destructive).
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 compact and well-structured: a one-sentence purpose followed by a bullet-style Args list. Every line adds value and there is no filler.
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 input semantics and includes practical constraints (default/max, examples, API key purpose). An output schema exists, so return values are documented elsewhere. It lacks only an explicit tie to sibling tools and authentication prerequisites, which would make it fully self-sufficient.
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 input schema has no descriptions for any parameters (0% coverage), but the description's Args list fully explains all four parameters: query (search term), state (2-letter filter), limit (default/max), and api_key (rate limits). This directly compensates for the schema gap.
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 opens with a specific verb and resource: 'Search WARN Act layoff notices by company name, city, or keyword.' This clearly defines what the tool does and differentiates it from siblings like get_company_layoffs (targeted company lookup) and get_recent_layoffs (time-based listing).
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 conveys clear context for use: it searches notices by free-text terms with optional state and limit filters. However, it does not explicitly contrast with sibling tools or state when not to use it, such as when querying a single known company (for which get_company_layoffs might be better).
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 have overlapping scopes: get_company_layoffs vs search_layoffs (company search), get_state_summary vs get_state_intelligence (state-level view), and get_market_pulse vs get_stats (overall statistics). While descriptions clarify some differences, an agent could easily misselect between these pairs.
All tool names follow a verb_noun snake_case pattern, with the majority starting with 'get_' and the rest being clear single verbs (export_records, search_layoffs, authenticate). The naming is consistent and predictable.
12 tools is well within the ideal 3-15 range. Each tool serves a discrete purpose (auth, export, queries, analytics, pricing, stats), and the count feels appropriate for a comprehensive data API.
The tool set covers querying WARN data by various dimensions, bulk export, cross-dataset analytics, and meta operations. Minor gaps include direct access to individual non-WARN datasets and a tool to fetch a single layoff notice by ID, but these are workarounds via search or export.