Get a single-call market snapshot across all 6 datasets.
Returns: WARN stats (30d trend), top industries, at-risk companies,
LCA/H-1B counts, DOL claims, SEC filings, bankruptcies, JOLTS snapshot.
Args:
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| 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 the full burden. It discloses rate-limit behavior via 'Optional API key for higher rate limits' and lists return contents, but does not explicitly state read-only behavior or other side effects/caveats.
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 front-loaded with the core purpose, followed by a structured Returns list and an Args section. Every line contributes value without redundancy.
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 tool is a broad aggregate with an output schema present, so the description's Returns list sufficiently covers what to expect. It lacks explicit authentication requirements or limitations, but the optional api_key line partially addresses access concerns.
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 schema has no descriptions for api_key (0% coverage), but the description compensates by explaining its purpose: 'Optional API key for higher rate limits.' This adds meaningful context beyond the schema's type and default.
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 starts with 'Get a single-call market snapshot across all 6 datasets,' which identifies a specific verb and resource. The Returns list further distinguishes it from narrower sibling tools like get_company_layoffs or get_state_intelligence.
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?
Usage is implied by 'single-call market snapshot' and 'across all 6 datasets' — it's positioned as the comprehensive overview tool. However, it does not explicitly state when to use it over alternatives or mention any exclusions.
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.