WARN Firehose

get_state_intelligence

Get a unified state profile combining all 6 datasets for a US state.

Returns WARN notices, LCA petitions, H-1B approvals/denials, DOL claims,
bankruptcy matches, JOLTS data, and a composite distress score.
Requires Pro tier or higher. Get your API key at warnfirehose.com/account

Args:
    state_code: Two-letter state abbreviation (e.g. CA, TX, NY)
    api_key: Your WARN Firehose API key (Pro tier required)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`api_key`	No
`state_code`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the burden of behavioral transparency. It discloses Pro tier auth requirement and lists the specific datasets returned, which adds context. However, it does not explicitly state that this is a read-only operation or address possible errors or rate limits, making it adequate but not exemplary.

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

Conciseness5/5

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

The description is compact and front-loaded with the core purpose, followed by return data, auth info, and args. No filler or redundant phrasing.

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 tool has an output schema, so return values don't need detailing. The description covers the data sources, auth requirement, and parameter meanings. The only minor gap is clarifying whether api_key is optional given the schema's default null, but overall it's complete for a data retrieval tool.

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?

The schema provides no descriptions, so the description fully compensates by explaining state_code as a two-letter abbreviation and api_key as the Pro tier requirement. Both parameters are meaningfully explained beyond the bare schema.

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

Purpose4/5

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

The description clearly states the tool 'Get a unified state profile combining all 6 datasets for a US state,' which is a specific verb+resource. It also lists the datasets returned, but it does not explicitly differentiate from the sibling tool get_state_summary, so it stops short of a perfect 5.

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

Usage Guidelines3/5

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

The description implies this tool is for comprehensive state analysis by mentioning 'all 6 datasets' and the composite distress score, but it does not explicitly state when to use this tool versus simpler alternatives like get_state_summary, nor does it mention any exclusions.

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

A3.9/5.0

Disambiguation3/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Resources

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