Get a summary of WARN Act layoff data for a specific US state.
Args:
state: Two-letter state code (e.g. CA, TX, NY, FL)
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | ||
| 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 must carry the full burden of disclosing behavior. It only mentions that api_key is optional for 'higher rate limits' but does not disclose whether the operation is read-only, requires authentication, handles invalid states, or what errors may occur. This is insufficient for a tool with no annotation support.
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 concise and front-loaded with a clear verb and resource. The Args block efficiently documents both parameters without unnecessary text. Every sentence serves a purpose.
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 presence of an output schema, the description covers the essential parameters and purpose. However, it lacks guidance on when to choose this tool over similar siblings and does not mention authentication prerequisites, making it slightly incomplete for an agent operating in a broader toolset.
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 meaningful context beyond the schema by explaining that state is a two-letter code with examples (CA, TX, NY, FL) and that api_key is optional for higher rate limits. This compensates for the 0% schema description coverage, though it could provide more detail on accepted formats or validation.
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's function: 'Get a summary of WARN Act layoff data for a specific US state.' It specifies the verb (get), the resource (summary of WARN Act layoff data), and the scope (specific US state), which distinguishes it from siblings like get_state_intelligence or get_recent_layoffs.
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 the tool is for state-level summary data but does not explicitly state when to use it over alternatives like get_state_intelligence or search_layoffs. There is no mention of when not to use it or which scenarios favor this tool.
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.