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Australian Consumer Data Right Product Data - CDR Explorer

by au.com.cdrexplorer

Get Single Product Constraints

get_product_constraints
Read-onlyIdempotent

Returns the constraints that apply to a specific banking product (identified by Product ID) offered by a Data Holder, as defined by the CDR Banking standards. Constraints describe eligibility and usage limits such as minimum/maximum loan-to-value ratio (MIN_LVR, MAX_LVR), minimum/maximum balance, minimum/maximum credit limit, and minimum deposit. Use GetDataHoldersByCategory first to obtain a valid Data Holder Brand ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
productIdYesThe unique identifier of the product whose constraints should be retrieved
dataHolderBrandIdYesThe unique brand identifier of the Data Holder, as returned by GetDataHoldersByCategory

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds domain context and a prerequisite, but does not describe return format, error behavior, or other operational traits beyond that.

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?

Three efficient sentences: the main purpose, the meaning of constraints with useful examples, and the prerequisite sequence. No wasted words, and the important invocation context is front-loaded.

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 simple read-only two-parameter tool, it covers the required parameters and the prerequisite call. The lack of an output schema is partially mitigated by describing constraint types, but it could be more complete about whether all constraint types are returned or how the response is structured.

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 both parameters are already documented. The description mostly repeats that productId identifies the product and dataHolderBrandId must come from GetDataHoldersByCategory, adding little beyond the schema.

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 states the tool returns constraints for a specific banking product identified by Product ID, which is a specific verb and resource. It does not explicitly distinguish itself from the similar sibling get_product_constraints_by_type, relying mainly on the singular 'specific product' phrasing.

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 instructing the agent to call GetDataHoldersByCategory first to obtain a valid Data Holder Brand ID. However, it does not mention when to prefer this tool over get_product_constraints_by_type or get_products_constraints, so alternatives are not explicitly addressed.

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

A3.5/5.0
Disambiguation3/5

Tools are generally distinct in purpose (data holders, categories, constraints, etc.), but significant overlap exists between singular and plural variants (e.g., get_product_constraints vs get_products_constraints) and between generic and filtered variants (get_product_constraints vs get_product_constraints_by_type). Although descriptions clarify the differences, the names are too similar, leading to potential misselection.

Naming Consistency3/5

The naming follows a consistent 'get_<resource>_<detail>' pattern with snake_case. However, there is an inconsistency between singular ('product') and plural ('products') prefixes, and the inclusion of 'hello' breaks the pattern entirely. Overall, the pattern is recognizable but not uniform.

Tool Count3/5

With 17 tools, the count is on the higher side for a specialized data server. Several tools are essentially duplicates (singular vs. plural) that could be merged with a parameter. The 'hello' tool adds no value. The number of tools feels slightly bloated for the apparent domain, but not excessively so.

Completeness2/5

The tool set focuses on retrieving specific attributes (constraints, fees, features) for known product IDs, but lacks any tool to list or search for products. Without get_products or similar, an agent cannot discover product IDs needed to use the other tools, creating a critical workflow gap. Minor operations like product creation or update are not expected, but basic product listing is missing.

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