AI Layoffs
Source-cited register of layoffs linked to AI, plus a live 0-100 AI job-loss index. No key.
我该使用它吗
质量与安全性
基于对工具定义和协议合规性的自动分析。
上下文开销
这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高,可用于其他任务的注意力就越少。
安装
一键安装
将以下内容添加到你的 `claude_desktop_config.json` 文件中:
{
"mcpServers": {
"ai-layoffs": {
"url": "https://ailayoffs.org/mcp"
}
}
}远程端点
https://ailayoffs.org/mcpstreamable-http它能做什么
工具清单
工具(4)
🟢get_ai_layoffs_index
Current reading of the AI Layoffs Index, a 0-100 score of AI-attributed job-loss pressure scaled against AI's own history since 2023 (not a share of all jobs). Returns the value, its band, the uncertainty range, the change vs last month, the three weighted components with what each reads, the as-of date, and a ready-made citation string. Takes no arguments.
输入模式
{
"type": "object",
"properties": {}
}🟢count_ai_job_losses
Answers 'how many jobs has AI replaced (or cost) this year?' with the register's published count: roles disclosed in layoffs linked to AI, roles where the employer itself named AI, the evidence-weighted headline figure, and the independent Challenger, Gray & Christmas count of US cuts attributed to AI. Each figure carries its scope (worldwide or US), its period and its definition, plus the ready-made answer sentence. The totals are computed over the whole register, so they are the published figures, not a sum of search results. Takes no arguments.
输入模式
{
"type": "object",
"properties": {}
}🟢search_ai_layoff_events(query, company, attribution, evidence_tier, execution, ...)
Search the source-cited register of layoffs linked to AI (one row per event, 2023 to date). Filter by company, free text, attribution, evidence tier, execution status, country, sector, affected role and date range; call with no arguments for the most recent events. Each event returns the employer's own stated reason (claim), roles counted vs reported but not counted, execution status, and a link to its primary source and its ailayoffs.org company page; set full_context for each event's longer context paragraph.
输入模式
{
"type": "object",
"properties": {
"query": {
"type": "string",
"maxLength": 200,
"pattern": "\\S",
"description": "Free text matched against the company, the stated reason, the context paragraph, sector, country, affected roles and source name. Every word must appear. Example: 'customer service'."
},
"company": {
"type": "string",
"maxLength": 120,
"pattern": "\\S",
"description": "Company name or part of it, e.g. 'Klarna'."
},
"attribution": {
"type": "array",
"items": {
"type": "string",
"enum": [
"explicit",
"mixed",
"blamed"
]
},
"description": "Keep only these attribution levels. explicit: The company itself declared the layoff AI-related. blamed: A credible source named AI, but the company did not. Shown as context and never counted: prong 1 of our standard requires the employer's own source to name AI, so a blamed event is a failed claim, not a discounted one. mixed: AI was cited alongside other material factors (cost, demand)."
},
"evidence_tier": {
"type": "array",
"items": {
"type": "string",
"enum": [
"tier1",
"tier2",
"tier3"
]
},
"description": "Keep only these evidence tiers. tier1 (Primary-source attributed): AI named as a workforce driver in the company's own SEC filing, on-record earnings call, or official statement, with the event corroborated by a structured source. tier2 (Reputable-press attributed): AI named as a cause by credible journalism quoting a named company source, but not yet in a company filing. tier3 (Inferred / single-source): Attribution from one secondary tracker, an unnamed source, or vague forward-looking language."
},
"execution": {
"type": "array",
"items": {
"type": "string",
"enum": [
"executed",
"partial",
"announced",
"reversed",
"unknown"
]
},
"description": "Keep only these execution statuses. executed: The reduction has been carried out. partial: Some of the cut is done, the rest pending. announced: A stated plan, not yet carried out (often multi-year). unknown: Execution status not established. reversed: The cut was rolled back or rehired against (e.g. Commonwealth Bank)."
},
"country": {
"type": "string",
"maxLength": 80,
"pattern": "\\S",
"description": "Country as recorded, partial match, e.g. 'United States', 'India', 'Sweden'. For US-based roles use us_only."
},
"us_only": {
"type": "boolean",
"description": "Only events whose affected roles are US-based."
},
"sector": {
"type": "string",
"maxLength": 80,
"pattern": "\\S",
"description": "Sector, partial match, e.g. 'Financial', 'Software'."
},
"role": {
"type": "string",
"maxLength": 80,
"pattern": "\\S",
"description": "Affected function or occupation, partial match, e.g. 'customer service', 'engineering'."
},
"since": {
"type": "string",
"pattern": "^\\d{4}(-\\d{2}(-\\d{2})?)?$",
"description": "Earliest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD."
},
"until": {
"type": "string",
"pattern": "^\\d{4}(-\\d{2}(-\\d{2})?)?$",
"description": "Latest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD."
},
"counted_only": {
"type": "boolean",
"description": "Only events whose roles the register counts as AI-attributed (drops events that are reported but not counted)."
},
"sort": {
"type": "string",
"enum": [
"newest",
"oldest",
"largest"
],
"description": "newest first (default), oldest first, or the largest disclosed cut first."
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 50,
"description": "Maximum events to return (default 10, at most 50)."
},
"full_context": {
"type": "boolean",
"description": "Also return each event's context paragraph. Several times longer, so best with a small limit; get_company_ai_layoffs always includes it for one company."
}
},
"additionalProperties": false
}🟢get_company_ai_layoffs(company)
Did a specific company cut jobs because of AI? Returns the register's verdict for that company (explicit, mixed or blamed), the roles it disclosed vs the roles counted as AI-attributed, and every recorded event with the employer's own words and primary source. Accepts a company name such as 'Klarna' or 'Salesforce'. If the company is not in the register, says so and what that does and does not mean.
输入模式
{
"type": "object",
"properties": {
"company": {
"type": "string",
"minLength": 1,
"maxLength": 120,
"pattern": "\\S",
"description": "Company name, e.g. 'Klarna', 'IBM', 'Salesforce'. Partial names work."
}
},
"required": [
"company"
],
"additionalProperties": false
}社区
证据