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AI Layoffs

by org.ailayoffs
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Search the AI layoffs register

search_ai_layoff_events
Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNoAffected function or occupation, partial match, e.g. 'customer service', 'engineering'.
sortNonewest first (default), oldest first, or the largest disclosed cut first.
limitNoMaximum events to return (default 10, at most 50).
queryNoFree text matched against the company, the stated reason, the context notes, sector, country and affected roles. Every word must appear. Example: 'customer service'.
sinceNoEarliest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD.
untilNoLatest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD.
sectorNoSector, partial match, e.g. 'Financial', 'Software'.
companyNoCompany name or part of it, e.g. 'Klarna'.
countryNoCountry as recorded, partial match, e.g. 'United States', 'India', 'Sweden'. For US-based roles use us_only.
us_onlyNoOnly events whose affected roles are US-based.
executionNoKeep 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).
attributionNoKeep 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_onlyNoOnly events whose roles the register counts as AI-attributed (drops events that are reported but not counted).
full_contextNoAlso return each event's context paragraph and the register's caveats. Several times longer, so best with a small limit; get_company_ai_layoffs always includes them for one company.
evidence_tierNoKeep 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover read-only, idempotent, non-destructive behavior, so the description adds value with data-scope details: each event returns the employer's stated reason, counted vs reported roles, execution status, and source links. It also discloses the response-size impact of full_context. This goes beyond the structured annotations without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tightly packed sentences: purpose/scope, filter list and default behavior, and return fields with full_context guidance. No filler, no redundancy, and the most important information (what it searches) comes first.

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?

Given the tool's complexity (15 optional parameters, no output schema), the description provides sufficient high-level context: what the register contains, how to call it for recent events, what each event returns, and when to use full_context. The schema covers parameter specifics and the annotations cover safety. It does not mention pagination or error behavior, but those are not critical for this read-only tool with a limit parameter in the 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 description coverage is 100%, so the baseline is 3. The description summarizes the filter categories (company, attribution, evidence tier, etc.) but does not add meaning beyond the schema; it does add a mild semantic hint about the no-argument default returning recent events. The detailed schema descriptions for all 15 optional parameters carry 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 opens with a specific verb and resource: 'Search the source-cited register of layoffs linked to AI', plus a scope clarification ('one row per event, 2023 to date'). It enumerates the filter dimensions and the no-argument default, making it easy to distinguish from siblings like count_ai_job_losses (aggregation) and get_company_ai_layoffs (single-company retrieval).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear invocation guidance: 'call with no arguments for the most recent events' and 'set full_context for the longer context and caveats'. The full_context parameter description explicitly names get_company_ai_layoffs as an alternative for one company, providing some sibling differentiation. However, it does not explicitly compare against count_ai_job_losses or get_ai_layoffs_index in the main description.

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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