org.ailayoffs/ai-layoffs

Est-ce que org.ailayoffs/ai-layoffs est sûr ?

Au dernier audit MCP Hub, le serveur MCP « org.ailayoffs/ai-layoffs » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.

Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Les 4 outils

• count_ai_job_losses≈ 214 tokens

  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.

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "count_ai_job_losses", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• get_ai_layoffs_index≈ 162 tokens

  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.

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "get_ai_layoffs_index", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• get_company_ai_layoffs≈ 356 tokens

  Did a specific company cut jobs because of AI? Returns the register's verdict for that company (explicit, mixed or blamed: the strongest grade among its cuts), its answer (the strongest position its own words hold: declared, cited, conflicting, attrition, denied, not-named, or statement for a workforce statement with no cut), a plain-language summary, the roles it disclosed vs the roles counted as AI-attributed, and every recorded event with the employer's own words and source. It also lists the company's later cuts that the register checked and left out because neither the employer nor credible press linked them to AI (later_cuts_not_ai_linked, never counted), so a question about the newest layoff is answered either way. 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.

  Paramètres, retour & exemple (1)

  Paramètres

  • company · string · requis
    Company name, e.g. 'Klarna', 'IBM', 'Salesforce'. Partial names work.

  Exemple d'appel

  {"name": "get_company_ai_layoffs", "arguments": {"company": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• search_ai_layoff_events≈ 1110 tokens

  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 source and its ailayoffs.org company page; set full_context for each event's longer context paragraph.

  Paramètres, retour & exemple (15)

  Paramètres

  • role · string · optionnel
    Affected function or occupation, partial match, e.g. 'customer service', 'engineering'.
  • sort · string · optionnel · newest | oldest | largest
    newest first (default), oldest first, or the largest disclosed cut first.
  • limit · integer · optionnel
    Maximum events to return (default 10, at most 50).
  • query · string · optionnel
    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'.
  • since · string · optionnel
    Earliest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD.
  • until · string · optionnel
    Latest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD.
  • sector · string · optionnel
    Sector, partial match, e.g. 'Financial', 'Software'.
  • company · string · optionnel
    Company name or part of it, e.g. 'Klarna'.
  • country · string · optionnel
    Country as recorded, partial match, e.g. 'United States', 'India', 'Sweden'. For US-based roles use us_only.
  • us_only · boolean · optionnel
    Only events whose affected roles are US-based.
  • execution · array · optionnel
    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 · array · optionnel
    Keep only these attribution levels. explicit: The company itself declared the layoff AI-related, without citing other material factors. blamed: A credible source named AI, but the company did not. Shown as context and never counted: the register's standard requires the employer's own words 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 · boolean · optionnel
    Only events whose roles the register counts as AI-attributed (drops events that are reported but not counted).
  • full_context · boolean · optionnel
    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.
  • evidence_tier · array · optionnel
    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.

  Exemple d'appel

  {"name": "search_ai_layoff_events", "arguments": {"role": "texte", "sort": "newest", "limit": 1, "query": "texte", "since": "texte", "until": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

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Source

https://ailayoffs.org/mcp