Xearno Tools

UK Redundancy Pay Calculator — statutory entitlement 2026

uk_statutory_redundancy_pay

Read-only

How much redundancy pay you’re owed — from your age, your years of service and your weekly pay, on the 2026 limits. Redundancy pay in the UK has a formula, and it is stranger than most people expect. Your years of service are weighted by how old you were during each of them — a year past 41 is worth a week and a half’s pay, a year in your twenties or thirties is worth one, a year before 22 is worth half. Only your last twenty count. And the weekly pay that feeds the formula is capped at £751 however much you actually earn, with £22,530 the ceiling on the whole payment. That cap moves every April, which is why a general-purpose AI will usually quote you last year’s. The age-weighting runs backwards from your leaving date and drops any year straddling a birthday into the lower band — a small fiddly rule, easy to state and easy to get wrong. Northern Ireland sets its own higher limits; those are here too.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`age`	No	Your age at the dismissal (relevant) date The multiplier depends on your age DURING each backward-counted year of service, not just today’s age — this is the table walk general AI botches. Use your age on the date your employment ends.
`nation`	No	Where do you work? Northern Ireland sets its own limits — currently HIGHER than Great Britain’s: £783 weekly, £23,490 maximum.	gb
`weeklyPay`	No	Gross weekly pay (£) Before tax. If your pay varies, use the average over the 12 weeks before your notice day. Capped at £751 — high earners all get the same statutory figure.
`serviceYears`	No	Complete years of continuous service Only FULL years count — 9 years 11 months is 9. Under 2 years there is no statutory entitlement; over 20 only the most recent 20 count.
`dismissalDate`	No	When does (did) your employment end? Picks the statutory limits: £751 weekly / £22,530 max from 6 April 2026, £719 / £21,570 before. The limits re-uprate every April.	on-after-6-apr-2026

TDQS

A4.7/5.0

Behavior5/5

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

The description extensively discloses the calculation behavior beyond the readOnlyHint annotation: it explains the age-weighted formula, the weekly pay cap of £751, the £22,530 maximum, the backwards-counting rule for birthdays, and the Northern Ireland limits. This gives the agent a deep behavioral model of what the tool does with inputs.

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?

The description is a single, dense paragraph where every sentence adds value. It front-loads the purpose and then packs the formula, caps, and edge cases without repetition or fluff. It is long but efficient, making it a model of structured conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description makes it clear that the tool returns the monetary amount of redundancy pay owed. It covers the key variables (age, service, weekly pay, nation, dismissal date), the 2026 limits, the April uprating, and the NI differences, so the agent has all context needed to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage with detailed descriptions for each parameter, so the baseline is 3. The description adds meaningful context that ties parameters together (e.g., 'Only your last twenty count' clarifies serviceYears; 'a year past 41 is worth a week and a half’s pay' clarifies age weighting), pushing it to a 4.

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+resource: 'How much redundancy pay you’re owed' and then details the inputs (age, years of service, weekly pay) and the 2026 limits. This makes the tool's purpose unmistakable and distinguishes it from other financial calculators in the sibling list.

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?

The description provides clear context for when to use the tool (calculating statutory UK redundancy pay, including Northern Ireland variations) and even warns that 'a general-purpose AI will usually quote you last year’s' caps, implying this tool is the accurate alternative. However, it does not explicitly name alternative sibling tools or state when not to use it, so it falls short of a 5.

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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TDQS

A3.9/5.0

Disambiguation4/5

Each tool targets a distinct niche (e.g., specific country tax rules, loan types, or legal calculations), with detailed descriptions that clarify boundaries. However, the large number of tools (66) could cause some confusion for an agent trying to select the right one for a general query, especially when multiple tools relate to the same country.

Naming Consistency4/5

Tool names follow a mostly predictable pattern: lowercase words separated by underscores, often starting with a country name (e.g., 'uk_stamp_duty_sdlt') or a topic (e.g., 'compound_growth'). There are minor deviations, such as abbreviations ('npv_irr', 'sip') and varying use of verbs, but overall the naming is clear and consistent.

Tool Count3/5

At 66 tools, the server is unusually large and covers an extensive range of financial and legal calculators. While each tool justifies its existence, the count exceeds the typical well-scoped range (3–15), making the server feel bloated. A more modular design might improve coherence.

Completeness4/5

The tool set covers a wide array of domains: personal income taxes, property taxes, loan calculations, investment returns, and specific country regulations. Minor gaps exist (e.g., missing tools for corporate taxes, general retirement planning, or insurance), but the overall coverage is thorough and addresses many niche scenarios that general AI handles poorly.

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