Source-cited register of layoffs linked to AI, plus a live 0-100 AI job-loss index. No key.
Scored across 4 tools
The four tools target distinct query shapes: a headline aggregate count, a 0-100 index score, a per-company verdict, and a filtered event search. The main overlap is between get_company_ai_layoffs and search_ai_layoff_events (which also accepts a company filter), but the descriptions make the distinction clear: one returns a verdict plus non-AI-linked cuts, the other returns raw rows. count_ai_job_losses and get_ai_layoffs_index are adjacent but clearly differentiated by output type (counts vs a scaled score).
All names follow a clean snake_case verb_noun pattern (count_, get_, get_, search_). The only wobble is the domain noun itself shifting between ai_job_losses, ai_layoffs, and ai_layoff_events, which is a minor stylistic inconsistency rather than a structural one.
Four tools is well-matched to a read-only reference dataset: one aggregate total, one composite index, one entity lookup, one searchable register. Each covers a genuinely different question an agent would ask, and none is redundant enough to drop.
Coverage of the domain is strong: macro totals, an index with components and trend, per-company verdicts including negative findings, and event search across company, sector, country, role, attribution, tier, and date. The gap is that breakdowns by sector/country/role only exist as row-level filters, with no aggregate or time-series endpoint, so an agent must sum events itself to answer 'which sectors lost the most'.
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.
Inputs: no named parameters.
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.
Inputs: no named parameters.
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; for a notable layoff the register checked and found not linked to AI, it returns that answer with the employer's own reason and source.
Inputs: company (required).
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.
Inputs: role, sort, limit, query, +11 more.
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AI exposure scores (0-100) for ~1,000 U.S. occupations, and recent AI-attributed layoffs.
Task-by-task AI exposure assessment of a job (Spain): 0-100 index, task map, weekly plan.
Search pro jobs with AI exposure and layoff signals, score a résumé, look up WARN layoff notices.
Sourced register of open-weight AI model releases and AI news. Every field cites its source.
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