Get all WARN Act layoff notices for a specific company.
Args:
company: Company name to search for (partial match supported)
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | ||
| company | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It mentions partial matching and the optional API key for higher rate limits, but does not discuss pagination, default time range, required authentication, or read-only nature. This is moderate disclosure, leaving some 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?
The description is a concise two-sentence docstring, front-loading the main purpose and using an Args block for parameter details. Every sentence adds value with no wasted words.
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 existence of sibling tools like search_layoffs and get_recent_layoffs, the description lacks guidance on when to use this tool specifically. However, the output schema exists and parameters are simple, so it satisfies the basic need but is not fully complete for selection among siblings.
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 adds meaning to both parameters: company includes partial match behavior, and api_key explains its purpose (higher rate limits). Since the schema has 0% description coverage, this fully compensates and goes beyond the bare titles.
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 fetches all WARN Act layoff notices for a specific company, with partial match support. This differentiates it from sibling tools like get_recent_layoffs and search_layoffs by specifying a company-scoped resource.
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 use for company-specific queries by stating 'for a specific company' and partial match support, but does not explicitly address when to choose this over alternatives like search_layoffs or get_recent_layoffs, nor provide exclusion criteria. This is implied usage rather than explicit 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.