WARN Firehose — Labor Market Intelligence

get_risk_signal

Get companies ranked by composite distress signal across all datasets.

Combines WARN layoff volume/recency, SEC restructuring filings,
bankruptcy filings, and H-1B denial rates into a single risk score.
Levels: Critical (7+), Elevated (4-6), Moderate (2-3), Low (1).
Requires Starter tier or higher. Get your API key at warnfirehose.com/account

Args:
    state: Optional 2-letter state code to filter
    min_score: Minimum risk score (default 3)
    limit: Max results (default 15, max 50)
    api_key: Your WARN Firehose API key (Starter tier required)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`state`	No
`api_key`	No
`min_score`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently mentions the required Starter tier, the need for an API key, and the risk level thresholds. It does not cover error handling or pagination, but provides meaningful context beyond a simple 'get' operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, starting with the core purpose, then data sources, risk levels, auth note, and a clean arg list. It is slightly verbose with the API key URL but every sentence contributes useful information. The arg list is logically formatted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, composite scoring methodology, risk levels, authentication requirements, and all input parameters. Since an output schema exists, not explaining return values is acceptable. It lacks details on potential errors or sort order, but for a scoring tool this is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates by documenting all four parameters with clear meanings and defaults: state (2-letter code), min_score (minimum risk score), limit (max results with max 50), and api_key (required tier). This adds substantial value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Get companies ranked by composite distress signal across all datasets.' It clearly distinguishes this from siblings by explaining it combines WARN layoffs, SEC filings, bankruptcy filings, and H-1B denial rates into a single risk score, which is unique among the listed tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on what the tool does and its data sources, making it easy to infer when to use it. However, it does not explicitly mention alternatives or exclusions, such as 'use get_recent_layoffs for raw layoff data instead.' It falls short of the explicit when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0

Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Resources

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