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 burden. It discloses the returned data and mentions api_key for higher rate limits, which is useful. However, it doesn't explicitly state whether the call is read-only, whether authentication is required for basic access, or any error/limitation behavior.
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, leading with the core action and using a bulleted list to summarize outputs. No unnecessary words or repetition.
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?
For a complex snapshot tool, the description covers the main purpose, outputs, and the only parameter. Output schema exists, so extra return details are helpful but not required. It could mention limitations like data freshness, but it's sufficient for basic 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?
The schema provides only a bare optional string param with no description. The description explains 'api_key: Optional API key for higher rate limits', adding meaningful practical purpose beyond the 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 'Get a single-call market snapshot across all 6 datasets' and enumerates the return categories. This distinctly differentiates it from siblings like get_company_layoffs or get_recent_layoffs, which have narrower scope.
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 when you need a broad cross-dataset overview in one call, but it does not explicitly state when not to use it or name alternative tools. There's no clear exclusion criteria.
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.