Company Intel

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).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "queries": "<queries>"
    },
    "name": "layoff-tracker"
  }
}

Input Schema

Name	Required	Description
`queries`	Yes	One entry per company name (e.g. "Google") or sector (e.g. "tech", "fintech"). Each is searched as "{query} layoffs" in Google News.
`sinceDays`	No	Only count news published within this many days.
`maxConcurrency`	No	How many queries to check in parallel.

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 27, 2026

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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