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Glama

axion-gateway

by com.getaxionlabs

search_mtd_answers

Search 30 plain-English answers about UK Making Tax Digital for Income Tax — thresholds and start dates (£50k Apr 2026 / £30k Apr 2027 / £20k Apr 2028), quarterly update deadlines, digital records, penalties, exemptions, sign-up, software. Every answer is sourced line-by-line to named GOV.UK/HMRC pages, re-checked 14 August 2026. Returns the matching questions with short answers; call get_mtd_answer for the full text and sources.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results, default 5.
queryYesThe question or topic, e.g. 'when do landlords have to start', 'quarterly deadline', 'penalties for missing an update'.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does a good job: it discloses the return shape ('matching questions with short answers'), the source freshness ('re-checked 14 August 2026'), and intentionally omits full text/sources. This prevents an agent from expecting more than the tool delivers.

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?

Two dense, front-loaded sentences with no filler. The core action and scope come first, followed by sourcing trust signals and the sibling pointer. Every clause adds operational value.

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?

For a two-parameter search tool with no output schema or annotations, the description covers return behavior, source verification, and the full-text alternative. Minor gaps like result ranking or empty-result behavior are not critical enough to lower the score further.

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 coverage is 100%, so the 'query' and 'limit' parameters are already documented with examples and constraints. The description reinforces the topical scope but adds no new parameter syntax or semantics beyond the schema.

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 starts with a concrete verb and resource: 'Search 30 plain-English answers about UK Making Tax Digital for Income Tax.' It also distinguishes itself from the sibling detail tool by explicitly saying 'call get_mtd_answer for the full text and sources.'

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 clear usage context by explaining what this tool returns and directing the agent to get_mtd_answer when full text and sources are needed. It doesn't explicitly exclude other siblings, but the search-vs-detail relationship is unambiguous.

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
Disambiguation3/5

Several tools overlap in subject matter: borough_odds, violation_dismissal_rates, and plate_lookup all touch NYC hearing outcomes, while operator_route_lookup and popla_outcomes both expose POPLA operator records. The descriptions are detailed enough to separate them on close reading, but an agent could easily select the wrong one when asking for odds or appeal outcomes.

Naming Consistency3/5

The names are uniformly lowercase snake_case and readable, but the pattern is mixed: get_mtd_answer, list_doors, and search_mtd_answers use a verb_noun form, while borough_odds, popla_outcomes, and violation_dismissal_rates are noun phrases, and plate_lookup/operator_route_lookup use noun_lookup compounds. There is no single consistent naming convention across the set.

Tool Count5/5

Nine tools is within the ideal range and each tool represents a distinct, substantive data product or query endpoint. For a gateway spanning multiple domains, this is well-scoped and not bloated.

Completeness4/5

The main query surfaces are covered: NYC parking has plate, borough, and violation-type views; UK parking has operator routing, deadline calculation, and POPLA outcomes; and MTD has search plus full-answer retrieval. Minor gaps exist, such as IAS appeal outcomes and a way to list all MTD answers without searching, but these are workable rather than blocking.

Resources