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Tizona — verification and routing for autonomous agents

KOOKABURRA

award_pay_rate
Read-onlyIdempotent

Minimum pay rates for an Australian modern award, from the Fair Work Commission MAPD. Input an award code (e.g. "MA000009") or fixed id, optionally filtered by classification level or employee-rate-type. Returns each classification's base and calculated rate with operative dates, verbatim from the FWC and stamped with version/published-year (CC BY 4.0). Dollar values only: does NOT say which award or classification applies, or what is payable — read with the award, which prevails. Not legal advice; unknown award throws.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
awardYesModern award code (e.g. "MA000009") or numeric award_fixed_id.
employeeRateTypeNoFilter by employee_rate_type_code, e.g. "AD" for adult. Comma-delimited for several.
classificationLevelNoNarrow to one classification level (1 = lowest).

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark this as read-only, open-world, and idempotent, and the description adds meaningful behavioral detail beyond them: data is verbatim from FWC, stamped with version/published-year, CC BY 4.0, unknown awards throw, and the result is not legal advice. This is rich context for an agent deciding to call the tool.

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 four sentences, front-loads the core purpose, and every sentence earns its place: what is returned, how to filter, provenance/versioning, scope limits, and error behavior. There is no filler or repetition of the title or annotations.

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?

Given there is no output schema, the description fully compensates by describing the return content (base and calculated rates with operative dates), filtering options, data source, licensing, caveats, and the unknown-award error. An agent has enough information to decide when and how to call this tool.

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 description coverage is 100%, so the baseline is 3. The tool description lightly echoes 'award code or fixed id' and 'filtered by classification level or employee-rate-type', but it adds no parameter-level detail beyond what the schema already provides; therefore it does not deserve a higher score.

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 states a specific operation and resource: returning 'Minimum pay rates for an Australian modern award' from the Fair Work Commission MAPD. It clearly distinguishes the tool from siblings like award_allowances and award_penalties by emphasizing 'Dollar values only' and pay-rate scope.

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 context on how to invoke the tool (award code or fixed id, optional filters) and explicit exclusions: it does not say which award applies or what is payable, and should be read with the award. It does not name alternative sibling tools directly, so it stops just short of fully explicit routing guidance.

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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