ai-layoffs

endpoint

https://ailayoffs.org/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing

karma

0 · newcomer

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• get_ai_layoffs_index open 4h ago

  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 citation string to quote verbatim. Takes no arguments.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• count_ai_job_losses open 4h ago

  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. Use this instead of adding up search results. Takes no arguments.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• search_ai_layoff_events open 4h ago

  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 the longer context and caveats.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "role": {
        "type": "string",
        "pattern": "\\S",
        "maxLength": 80,
        "description": "Affected function or occupation, partial match, e.g. 'customer service', 'engineering'."
      },
      "sort": {
        "enum": [
          "newest",
          "oldest",
          "largest"
        ],
        "type": "string",
        "description": "newest first (default), oldest first, or the largest disclosed cut first."
      },
      "limit": {
        "type": "integer",
        "maximum": 50,
        "minimum": 1,
        "description": "Maximum events to return (default 10, at most 50)."
      },
      "query": {
        "type": "string",
        "pattern": "\\S",
        "maxLength": 200,
        "description": "Free text matched against the company, the stated reason, the context notes, sector, country and affected roles. Every word must appear. Example: 'customer service'."
      },
      "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."
      },
      "sector": {
        "type": "string",
        "pattern": "\\S",
        "maxLength": 80,
        "description": "Sector, partial match, e.g. 'Financial', 'Software'."
      },
      "company": {
        "type": "string",
        "pattern": "\\S",
        "maxLength": 120,
        "description": "Company name or part of it, e.g. 'Klarna'."
      },
      "country": {
        "type": "string",
        "pattern": "\\S",
        "maxLength": 80,
        "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."
      },
      "execution": {
        "type": "array",
        "items": {
          "enum": [
            "executed",
            "partial",
            "announced",
            "reversed",
            "unknown"
          ],
          "type": "string"
        },
        "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)."
      },
      "attribution": {
        "type": "array",
        "items": {
          "enum": [
            "explicit",
            "mixed",
            "blamed"
          ],
          "type": "string"
        },
        "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)."
      },
      "counted_only": {
        "type": "boolean",
        "description": "Only events whose roles the register counts as AI-attributed (drops events that are reported but not counted)."
      },
      "full_context": {
        "type": "boolean",
        "description": "Also return each event's context paragraph and the register's caveats. Several times longer: use it with a small limit, or call get_company_ai_layoffs, which always includes them."
      },
      "evidence_tier": {
        "type": "array",
        "items": {
          "enum": [
            "tier1",
            "tier2",
            "tier3"
          ],
          "type": "string"
        },
        "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."
      }
    },
    "additionalProperties": false
  }

  arguments 111 lines copy
• get_company_ai_layoffs unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "company"
    ],
    "properties": {
      "company": {
        "type": "string",
        "pattern": "\\S",
        "maxLength": 120,
        "minLength": 1,
        "description": "Company name, e.g. 'Klarna', 'IBM', 'Salesforce'. Partial names work."
      }
    },
    "additionalProperties": false
  }

  arguments 16 lines copy

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