au-opendata-mcp

Validate Abn

validate_abn

Validate an Australian Business Number against the ATO's official checksum to confirm validity without manual modulus-89 calculation.

Instructions

Validate an Australian Business Number against the ATO's official checksum.

Use this instead of judging an ABN yourself — the check is a deterministic modulus-89 calculation and guessing gets it wrong.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "abn": "<abn>"
    },
    "name": "validate_abn"
  }
}

Input Schema

Name	Required	Description	Default
`abn`	Yes	11 digits, with or without spaces or hyphens.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 15, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description still discloses the behavioral profile that matters: the check is deterministic and authoritative (modulus-89 against the ATO checksum), which tells the agent the result is trustworthy and not heuristic. It doesn't mention error behavior for malformed input, but as a stateless pure validator that is a minor gap.

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 short sentences with no filler; the what is front-loaded and the why/when follows immediately. Every clause earns its place.

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?

For a one-parameter validator with an output schema present, the description supplies everything the agent needs: what is validated, against what authority, and why to prefer it over inference. Return format is covered by the output schema, so nothing essential is missing.

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% and the single parameter's format ('11 digits, with or without spaces or hyphens') is fully documented in the schema. The description adds no parameter-level detail beyond that, so the baseline 3 applies.

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 gives a precise verb plus resource ('Validate an Australian Business Number') and even names the authoritative mechanism ('ATO's official checksum'). No sibling tool in the set performs validation, so the scope is unambiguous at a glance.

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 explicitly tells the agent when to use it ('Use this instead of judging an ABN yourself') and names the alternative it is displacing — self-reasoning. It gives the rationale (deterministic modulus-89), though it offers no explicit exclusions or edge-case conditions.

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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