Get the most recent WARN Act layoff notices.
Args:
days: Look back this many days (default 30)
state: Optional state filter (2-letter code)
limit: Max results (default 25, max 100)
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No | ||
| 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, the description carries some behavioral burden. It discloses the optional API key for higher rate limits, which is useful. However, it omits other behavioral details such as output format, sorting, pagination, or error handling. The read-only nature is implied by 'Get' but not explicitly stated.
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 appropriately sized: a one-line summary followed by a clear, structured Args list. Each parameter gets a concise explanation. No redundant or irrelevant text.
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?
Given the tool's simplicity and the existence of an output schema, the description adequately covers purpose and parameters. It does not explain return values (not needed due to output schema) but lacks some usage context and behavioral nuance that would make it fully complete for agent invocation.
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%, but the description explains every parameter: days (look back period), state (optional filter), limit (max results), and api_key (rate limits). This fully compensates for the schema's lack of descriptions and adds meaningful semantics.
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 'Get the most recent WARN Act layoff notices' with a specific verb and resource. While it doesn't explicitly differentiate from sibling tools like get_company_layoffs or search_layoffs, the focus on 'recent' and 'WARN Act' provides enough specificity for most use cases.
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?
There is no explicit guidance on when to use this tool versus alternatives like search_layoffs or get_company_layoffs. The description implies it's for recent notices but doesn't mention exclusions, prerequisites, or selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.