Xearno Tools

UK Universal Credit Taper — Does Working More Pay?

uk_universal_credit_taper

Read-only

What an extra shift or pay rise really leaves you on Universal Credit — the 55% taper, the work allowance you may not have, and the pension trick. Computes your Universal Credit payment at your current net earnings and at your earnings plus the raise or extra shift you are weighing — showing exactly how much of the extra you keep after the 55% taper. The taper applies to NET earnings (after tax, NI, and 100% of pension contributions), the work allowance only exists for households with children or limited capability for work, and whether your UC includes a housing element switches that allowance between £427 and £710 a month. General AI gets all three wrong: it tapers gross pay, hands everyone an allowance, and quotes outdated rates.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`household`	No	Your household Sets the standard allowance — the base of your maximum UC award. Couples claim jointly and their earnings are combined.	single25
`hasChildren`	No	Children on the claim? The work-allowance gate. Only households responsible for a child OR with limited capability for work (LCW/LCWRA after a Work Capability Assessment) get a work allowance. Answer "yes" if either applies. Everyone else is tapered from the first pound of net earnings.	yes
`extraEarnings`	No	Extra net earnings you are considering (£) The raise, extra shift, or overtime you are weighing — as extra NET (take-home) pay per month. The tool shows how much of it survives the taper.
`otherElements`	No	Other UC elements on your statement (£) Child, housing, disability, and carer elements from your UC statement — add them so the taper math starts from your real maximum award. Left at 0, the tool uses only the standard allowance and will understate your actual payment (though the keep-rate on extra earnings is unaffected while UC stays above £0).
`housingElement`	No	Does your UC include a housing element? The hidden switch. If your UC award includes help with housing costs, your work allowance is £427/month; with no housing element it is £710. Check your UC statement, not your tenancy — it is about what is IN the award.	yes
`netMonthlyEarnings`	No	Your net monthly earnings (take-home) (£) Take-home pay per assessment month — after income tax, National Insurance, AND 100% of your pension contributions. UC tapers NET earnings, not gross: this is the number on your bank statement, and pension contributions reduce it pound-for-pound.

TDQS

A4.5/5.0

Behavior5/5

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

The description extensively reveals behavioral details beyond the readOnlyHint annotation: it explains the taper logic, work allowance prerequisites, housing element switching, and that pension contributions reduce net earnings. No contraction 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.

Conciseness4/5

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

The description is dense with information but front-loaded with purpose. Each sentence adds value, though it is longer than necessary. Structure is logical: purpose, key rules, common errors.

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 complexity of UK Universal Credit, the description covers essential aspects: taper rate, work allowance conditions, housing element distinction, and net earnings definition. It does not describe output format but the tool's computation is straightforward.

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?

Schema coverage is 100% with detailed parameter descriptions. The tool description adds extra meaning by explaining the overarching logic (e.g., why pension contributions count) and common pitfalls, providing value beyond schema alone.

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 clearly states the tool's purpose: computing Universal Credit payment at current and increased earnings to show taper impact. It includes specific details like the 55% taper, work allowance conditions, and common mistakes, making the purpose distinct from sibling tools.

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 usage context: when considering an extra shift or pay rise and wanting to see UC impact. It lacks explicit guidance on when not to use or alternatives among siblings, but the context is clear and helpful.

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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