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Company Intelligence Tools — Zinin M2M Hub

Layoff Tracker

layoff-tracker
Read-only

Track recent tech layoffs by company name or sector (e.g. "fintech", "AI") straight from Google News — a demand and recruiting signal. No login, no scraping, no proxies. Returns matched headlines, mention count and a one-line summary per query. — $0.01/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queriesYesOne entry per company name (e.g. "Google") or sector (e.g. "tech", "fintech"). Each is searched as "{query} layoffs" in Google News.
sinceDaysNoOnly count news published within this many days.
maxConcurrencyNoHow many queries to check in parallel.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: 'No login, no scraping, no proxies,' cost ($0.01/call), and return format (headlines, mention count, summary). It goes beyond annotations without contradicting them, though rate limits or update frequency could add more depth.

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 concise at roughly three sentences, front-loading the core purpose and resource. It includes necessary details without excess, though the cost and payment info, while transparent, could be seen as slightly tangential to tool selection. Still, it's efficient and well-structured.

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?

For a simple three-parameter tool with no output schema, the description covers the main aspects: purpose, source, output format, cost, and lack of authentication requirements. Missing details like example output or error scenarios are minor given the low complexity. The description adequately complements annotations and schema.

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

Parameters3/5

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

Schema coverage is 100% with each parameter having a clear description. The tool description restates some parameter info but does not add new semantic meaning beyond what is already in the schema. Baseline 3 is appropriate since the schema already does the heavy lifting.

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 clearly states it tracks recent tech layoffs by company or sector from Google News, with a specific purpose as a demand and recruiting signal. This distinctively separates it from sibling tools like hiring-trend-index or company-hiring-radar, which focus on positive hiring signals.

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 usage for layoff-related signal gathering but does not explicitly state when to use this tool versus alternatives or when not to use it. No direct comparisons to sibling tools are provided, so the agent must infer the context from the tool's purpose alone.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some overlap exists between company-lookup and company-registry-enricher, and between individual signal tools and composite rollups. The descriptive names help differentiate, but the boundary between one-off screenings and alert/rollup tools requires careful reading.

Naming Consistency4/5

Tool names follow a consistent pattern of hyphenated lowercase nouns (e.g., company-lookup, funding-alert, sanctions-screening). The one exception, pricing_info, uses an underscore, creating a minor deviation from the otherwise uniform naming style.

Tool Count4/5

With 20 tools, the server is on the higher end of typical scope but justified for a comprehensive company intelligence bundle. Each tool covers a distinct or complementary aspect of company research, so the count feels purposeful rather than bloated.

Completeness5/5

The toolset covers company lookup, registry, hiring, funding, sanctions, litigation, patents, contacts, new company detection, and email verification—a broad and well-rounded surface for due diligence and sales intelligence. Composite tools like intent-signal-aggregator and lead-list-qualifier tie these together effectively, leaving no major dead ends.

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