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Tizona — verification and routing for autonomous agents

APOSTLEBIRD

award_allowances
Read-onlyIdempotent

Allowances for an Australian modern award, from the Fair Work Commission MAPD. "kind"=wage (leading hand, first aid, tool) or expense (meal, travel, vehicle). By award code (e.g. "MA000009") or fixed id. Returns each allowance's amount, all-purpose flag and payment frequency, verbatim (CC BY 4.0). Dollar values only: does NOT determine which allowance applies — read with the award, which prevails. Not legal advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoWhich allowance register. Defaults to wage.
awardYesModern award code (e.g. "MA000009") or numeric award_fixed_id.

TDQS

A4.4/5.0
Behavior5/5

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

With annotations already indicating read-only, idempotent, and non-destructive behavior, the description adds valuable context: the MAPD source, CC BY 4.0 licensing, verbatim returns, and the returned fields (amount, all-purpose flag, payment frequency). It also discloses limitations clearly ('does NOT determine which allowance applies'). No contradiction with annotations exists.

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 compact and front-loaded: it states the resource, lookup scope, return contents, and limitations in four efficient sentences. Every sentence adds necessary information, and there is no redundant filler.

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?

There is no output schema, so the description correctly compensates by naming the return fields: amount, all-purpose flag, and payment frequency. It also covers input options, data source, licensing, and scope limitations. For a read-only lookup tool, this is sufficient for an agent to invoke it 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?

Schema coverage is 100%, so the schema already documents both parameters. The description adds meaning by giving concrete examples for 'kind' (leading hand, first aid, tool; meal, travel, vehicle) and clarifying that 'award' can be a code or a numeric fixed id. This goes beyond the schema's basic definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as returning allowances for Australian modern awards from the Fair Work Commission MAPD, with specific subcategories ('wage' or 'expense') and lookup methods. It does not explicitly name or differentiate sibling tools such as award_pay_rate or award_penalties, so it loses one point. Overall it is clear and actionable.

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 useful context on how to use the tool: by award code or fixed id, and that it returns dollar values only, not legal determinations. It tells the agent that the award prevails and that the tool is not legal advice, which helps scope appropriate use. It does not explicitly say when to choose this tool over sibling allowance-related tools.

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

A4.2/5.0
Disambiguation4/5

Most tools map cleanly to a distinct action and resource: single-URL lookup, batch triage, watch/pull lifecycle, and the entity-name operations are each clearly separated. The only likely confusion is between check_ai_crawler_access and verify_ai_crawler, but the descriptions draw that boundary well.

Naming Consistency4/5

Tool names overwhelmingly follow a verb_noun convention such as calculate_gst, verify_email_address, and normalise_entity_name. The non-verb award_pay_rate and the slightly awkward total_invoice and pull_ai_crawler_watch are minor deviations from an otherwise consistent pattern.

Tool Count5/5

Fourteen tools sits comfortably in the well-scoped range, and each cluster earns its place: entity matching, Australian compliance, email verification, and AI crawler access all have distinct tool groupings. Nothing feels redundant, and the count reflects the server's broad verification purpose without bloat.

Completeness4/5

The surface covers the core verification workflows well, including batch and watch variants for crawler access and a full set of entity-name operations. The main gap is that the server name promises routing but the tools mostly verify and triage rather than actively route; minor lifecycle niceties like unwatching are also absent.

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