Source-cited register of layoffs linked to AI, plus a live 0-100 AI job-loss index. No key.
Scored across 4 tools
Each tool targets a distinct need: one for the headline count, one for the index, one for a specific company's record, and one for the event-level search. There is no overlap in purpose.
Tool names follow a consistent verb-noun pattern: 'count', 'get', 'search' + object. Minor deviation: 'count_ai_job_losses' vs 'get_ai_layoffs_index' and 'get_company_ai_layoffs' — but the pattern is clear and predictable.
With 4 tools, the set is tightly scoped to the server's purpose of querying AI-layoff data. Each tool covers a distinct query type and there is no redundancy, making the count ideal.
The server provides aggregate counts, an index, a company-specific lookup, and a searchable event list, covering the main ways a user would query the data. A minor gap might be lack of direct filtering by specific metric or export, but the surface is sufficient for the intended use.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond that: counts are computed over the whole register, are published figures not sums of search results, and each figure includes scope, period, and definition. This helps set correct expectations for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat dense but every sentence contributes useful information: the question answered, the list of figures, the metadata carried, the computation method, and the lack of arguments. It is front-loaded with the main purpose and does not waste words, though it could be tightened slightly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-argument, read-only tool with full schema coverage and no output schema, the description is remarkably complete. It tells the agent what figures will be present, their scope/period/definition, and clarifies that totals are register-wide published numbers rather than search-derived sums. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, and the description states 'Takes no arguments,' matching the schema coverage of 100%. Per the rubric, a 0-parameter tool gets a baseline of 4, and no additional semantic detail is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly answers a specific question ('how many jobs has AI replaced...') with a specific resource (the register's published count), and distinguishes itself from siblings that fetch indexes, company-level details, or search events. It enumerates the exact figures returned, leaving no ambiguity about the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states the exact question this tool answers, which implicitly defines when to use it. It does not mention alternatives or exclusions, but the context is clear enough for an agent to select it for aggregate count queries. A 5 would require explicit sibling routing, which is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it readOnly, idempotent, and non-destructive, so the description only needs to add context. It adds the important scale interpretation ('scaled against AI's own history since 2023, not a share of all jobs') and lists the exact returned fields, including the as-of date and uncertainty range, which help the agent understand the result without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the tool's essential purpose and metric clarification, and then lists the output contents efficiently. The 'not a share of all jobs' caveat is placed early where it prevents misinterpretation, and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Because there is no output schema, the description takes on the burden of describing the return value, and it does so thoroughly (value, band, uncertainty range, change, components, as-of date, citation string). With no parameters, read-only annotations, and low complexity, nothing essential is missing for an agent to call this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so the schema is empty and there are no parameter semantics to document. The description explicitly confirms 'Takes no arguments,' which is more than enough; per the rubric, zero-parameter tools get a baseline of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource (the AI Layoffs Index) and a clear action (returns the current reading), and explains the metric's scale and scope ('0-100 score... not a share of all jobs'). This clearly differentiates it from sibling tools that count or search individual layoff events/company layoffs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to prefer this tool over its siblings (count_ai_job_losses, get_company_ai_layoffs, search_ai_layoff_events). The phrase 'Current reading' implies the tool is for the aggregate index snapshot, but there are no explicit when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| company | Yes | Company name, e.g. 'Klarna', 'IBM', 'Salesforce'. Partial names work. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. The description adds substantial behavioral context beyond that: the verdict categories, the disclosed-vs-attributed roles comparison, inclusion of every recorded event with employer's own words and primary source, and the not-found behavior ('says so and what that does and does not mean'). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with a logical flow: framing question, detailed return contents, and edge-case behavior. The opening sentence slightly echoes the title but serves as a natural lead-in. The rest is information-dense with no wasted words, earning a high-but-not-perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description comprehensively covers the return contract (verdict, roles comparison, events, sources) and the not-found case. It doesn't specify the exact response format, but enumerating the result components is enough for an agent to understand the outcome. Minor ambiguity remains about whether 'roles' are counts or lists, but overall it's complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema already explains the company parameter with examples and 'Partial names work.' The description only repeats that it accepts a company name like 'Klarna' or 'Salesforce,' adding no new parameter meaning. Baseline 3 is appropriate given full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: '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.' The verb 'Returns' and the resource (per-company AI-layoff details) are specific, and the scope 'specific company' distinguishes it from the aggregation/search/index siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description establishes when to use the tool: when you have a specific company name ('Accepts a company name such as 'Klarna' or 'Salesforce'') and want a verdict for that company. It does not explicitly name alternatives or exclusions, but the context is clear enough that an agent could infer the right selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Affected function or occupation, partial match, e.g. 'customer service', 'engineering'. | |
| sort | No | newest first (default), oldest first, or the largest disclosed cut first. | |
| limit | No | Maximum events to return (default 10, at most 50). | |
| query | No | 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 | No | Earliest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD. | |
| until | No | Latest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD. | |
| sector | No | Sector, partial match, e.g. 'Financial', 'Software'. | |
| company | No | Company name or part of it, e.g. 'Klarna'. | |
| country | No | Country as recorded, partial match, e.g. 'United States', 'India', 'Sweden'. For US-based roles use us_only. | |
| us_only | No | Only events whose affected roles are US-based. | |
| execution | No | 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 | No | 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 | No | Only events whose roles the register counts as AI-attributed (drops events that are reported but not counted). | |
| full_context | No | 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. | |
| evidence_tier | No | 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. |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
count_ai_job_lossesget_ai_layoffs_indexget_company_ai_layoffssearch_ai_layoff_eventsEvidence-graded, task-level record of how AI and automation change jobs and degrees. Read-only.
Search 5,950 US WARN Act layoff notices by state, employer, and date (CA, TX, NY, IL, NC).
Live AI data for agents: model releases, regulations (EU AI Act), GenAI glossary, daily news.
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