Return WARN Firehose pricing tiers, included tools, and signup URLs.
Use this when the user asks about pricing, hits a rate limit, or wants to upgrade. Returns tier prices, daily call limits, included datasets, and direct signup links so the user can act immediately.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the burden. It discloses what the tool returns (tier prices, daily call limits, included datasets, signup links) but does not mention any behavioral aspects like whether it makes network calls, requires authentication, or has side effects. For a simple read-only info tool, this is acceptable but not richly transparent.
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 two sentences with no fluff. The first sentence states the core purpose, and the second adds usage context and return details. It is front-loaded and every sentence earns its place. Excellent conciseness.
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 zero-parameter tool with no output schema, the description is fully complete. It covers what the tool does, when to use it, and what it returns. No additional information is needed for an agent to select and invoke it correctly.
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 tool has zero parameters, so schema coverage is trivially 100%. Per the rubric, a baseline of 4 is appropriate when there are no parameters. The description does not need to explain parameter meaning. It adds value by clarifying output details that are not in 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 'Return WARN Firehose pricing tiers, included tools, and signup URLs' with a specific verb and resource. This distinguishes it from sibling tools like get_company_layoffs or get_market_pulse, which focus on other domains. The purpose is unambiguous.
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 provides three trigger scenarios: 'when the user asks about pricing, hits a rate limit, or wants to upgrade.' This gives clear context for use. It does not mention when not to use or alternatives, but given the tool's unique purpose among siblings, that is a minor gap.
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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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.