Skip to main content
Glama

AI Layoffs

by org.ailayoffs
OverviewDiscussionsAdmin

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

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 paragraph, sector, country, affected roles and source name. 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, 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_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. Several times longer, so best with a small limit; get_company_ai_layoffs always includes it 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. Changed1 schema field changed
    • changedInput schema / properties / attribution / description
      Previous value: -"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)."New value: +"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)."
  2. Changed2 schema fields changed
    • changedInput schema / properties / full_context / description
      Previous value: -"Also 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."New value: +"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."
    • changedInput schema / properties / query / description
      Previous value: -"Free text matched against the company, the stated reason, the context notes, sector, country and affected roles. Every word must appear. Example: 'customer service'."New value: +"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'."
  3. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description goes beyond them by disclosing the return shape — the employer's own stated reason (claim), roles counted vs merely reported, execution status, and source plus ailayoffs.org company page — and by flagging the full_context payload-size tradeoff. That is meaningful behavioral context the annotations do not carry.

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

Conciseness4/5

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

Three sentences, front-loaded with the purpose and scope before the filter list and the return description. The middle sentence is dense but each clause maps to a distinct capability, so there is little wasted text; it is slightly long relative to what the schema already covers.

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?

With no output schema, the description carries the burden of describing returns and does so adequately (claim, counted vs reported roles, execution status, source link, optional context paragraph). Combined with 100% schema coverage on 15 params and clear annotations, an agent has enough to call the tool correctly; only the sibling-routing story is thin.

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% across 15 parameters, so the schema already documents every field, including enums and examples. The description restates the same filter list without adding syntax, defaults, or interaction rules beyond what the schema provides, so it earns the baseline 3 rather than more.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise verb+resource and scope: 'Search the source-cited register of layoffs linked to AI (one row per event, 2023 to date).' It also enumerates the filter dimensions, so an agent immediately knows what the tool is. Sibling differentiation is only partial — get_company_ai_layoffs is mentioned, but count_ai_job_losses and get_ai_layoffs_index are not contrasted, and that routing information is buried inside a parameter description rather than the main body.

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 a clear default path ('call with no arguments for the most recent events') and notes that full_context is 'best with a small limit' while get_company_ai_layoffs 'always includes it for one company', which steers the agent between tools. What is missing is explicit when-not-to-use guidance against the counting/index siblings, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All AI Layoffs toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources