UK vehicle tax (VED) bands, expensive-car supplement, EV rules and MOT fees, sourced.
Scored across 1 tool
There is only one tool, so there is no possibility of selecting the wrong tool or confusing overlapping purposes. The category parameter cleanly partitions the domain internally, and unknown categories return a covered:false response with nearest keys, avoiding silent failures.
With a single tool, naming consistency is trivially satisfied. The name uk_vehicle_rules_lookup follows a clear snake_case verb_noun pattern and matches the server name databutler-uk-vehicle-rules.
A single comprehensive lookup tool with a category parameter is a reasonable, idiomatic design for a read-only reference dataset, avoiding tool bloat. It is slightly under the typical 3-15 range, but the tool is not trivial: it aggregates ten distinct rule categories with rich per-figure provenance.
The surface covers the stated domain well: VED bands across registration eras, electric vehicles, historic exemptions, MOT rules and fees, and penalties, each with citations and freshness metadata. Minor gaps remain (e.g. no registration-based tax calculation or van/HGV-specific categories), but core reference workflows are covered.
UK vehicle tax (VED) and MOT rules for 2026-27, verified against gov.uk, by category: ved-first-year (first-year rate by CO2 band for cars registered on or after 1 April 2017, petrol/RDE2-diesel and other-diesel columns), ved-standard (the flat rate from the second year and the Direct Debit surcharges), ved-expensive-car (list price threshold, annual supplement, years applied), ved-2001-2017 (bands A–M), ved-pre-2001 (engine size), ved-electric (zero-emission cars taxable since 1 April 2025), historic (40-year MOT exemption and the vehicle-tax exemption build date), mot (first MOT at 3 years, maximum fees by class, early renewal, retests, exemptions) and penalties (no MOT, dangerous vehicle, untaxed vehicle). Each figure carries its value, unit, the quoted line from the page, its source URL, effectiveFrom and the previous year's value; the answer carries verifiedDate, freshness and a citation permalink. No category returns the listing; an unknown category answers covered:false with the nearest keys.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | one of: ved-first-year, ved-standard, ved-expensive-car, ved-2001-2017, ved-pre-2001, ved-electric, historic, mot, penalties (omit to list all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds substantial behavioral context beyond that: the per-figure evidence shape (value, unit, quoted line, source URL, effectiveFrom, prior-year value), the envelope fields (verifiedDate, freshness, citation permalink), and the explicit failure mode for unknown categories.
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?
Purpose and coverage list are front-loaded with no filler sentences. However, it is delivered as one very long run-on sentence, and some of the return-shape detail is dense; a slightly more structured form would read better, though little could be deleted without losing information.
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?
No output schema exists, so the description carries the return-value burden and does so thoroughly — evidence fields per figure, envelope metadata, citation permalink, and the unknown-category fallback. Combined with read-only annotations and a fully documented single parameter, an agent has everything needed to invoke and interpret the result.
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%, so a 3 baseline applies, but the description goes further by explaining what each category value actually covers (e.g. ved-electric = zero-emission cars taxable since 1 April 2025, historic = 40-year MOT exemption and build-date tax exemption). That is meaningful semantics beyond the bare list in the schema.
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?
States a specific resource (UK vehicle tax/VED and MOT rules) with year scope (2026-27) and source authority (gov.uk), then enumerates every category the tool can return. An agent knows exactly what it is getting and can map its question onto a specific category.
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?
Gives clear context: category values are enumerated, omitting it lists all, and an unknown category returns covered:false with the nearest keys. There are no sibling tools, so no alternative routing is needed, but the description never states when this tool is *not* appropriate (e.g. non-UK vehicles or non-2026-27 years).
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.
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Current UK tax-year rates and thresholds, verified against gov.uk with source URLs and dates.
UK Clean Air Zone daily charges by council, per vehicle class, and what a trip costs.
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