layoff_coverage_statsPer-state coverage of the WARN corpus: how many notices are held for each state and the most recent dates. Use it to check freshness before relying on a search result. Requires an API key.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It discloses the API key requirement and the read-only nature of reporting coverage statistics. It stops short of describing output details or limitations, but for a zero-parameter query tool this is reasonable.
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?
Two sentences, no filler. The first sentence states what the tool returns, and the second gives the use case and a key requirement. The description is well structured and easy to scan.
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 simple zero-parameter coverage query, the description covers the core purpose, use case, and authentication requirement. It lacks a schema or explicit output format, but the values the agent needs to see—state, count, and recent date—are clearly implied.
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?
There are zero parameters, so the baseline is 4. The description correctly avoids documenting nonexistent parameters and does not need to compensate for any schema gaps.
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 specifies exactly what the tool provides: per-state counts of notices and the most recent dates for the WARN corpus. It names the resource and output clearly, and the phrase 'check freshness before relying on a search result' differentiates it from the search_layoff_notices sibling.
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 explicitly states when to use this tool: before relying on a search result, to verify freshness. This gives clear contextual guidance, though it does not enumerate when-not-to-use or mention the describe_dataset sibling.
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.
Each tool has a distinct purpose: one describes the dataset, one provides coverage/freshness stats, and one performs the actual search. There is no overlap or ambiguity between them.
All names use snake_case and are descriptive, with two following a verb_noun pattern (describe_dataset, search_layoff_notices). layoff_coverage_stats is a noun phrase rather than a verb-led name, but it is still clear and consistent in style.
Three tools are well-scoped for a single dataset server, covering dataset explanation, coverage/freshness, and search. Each tool earns its place without redundancy.
The core workflow of describing, checking coverage, and searching WARN layoff notices is covered. A minor gap is the lack of a record-fetch-by-ID or export tool, but search returns structured records with provenance keys, so the domain is essentially complete.