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Validate records against schema

validate_records
Read-onlyIdempotent

Validate flat payment records against ISO 20022 message type schemas. Returns row-by-row structural error reports to catch issues before message generation.

Instructions

Validate flat records against a message type's input JSON Schema.

Use this before ``generate_message`` to catch structural/type errors
per record and get a row-by-row error report. This checks JSON-Schema
shape only; for payment-scheme rulebook checks (SEPA field lengths,
charset, etc.) also run ``validate_payment_scheme``.

Returns a report ``{"valid": bool, "total": int, "valid_count": int,
"errors": [...]}``.

Args:
    message_type: A supported ISO 20022 pain message type.
    records: One or more flat payment records to validate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordsYesOne or more flat payment records to validate, each a dict of field name → value (see get_input_schema for the fields and get_required_fields for the mandatory ones).
message_typeYesA supported ISO 20022 pain message type. Must be exactly one of: 'pain.001.001.03', 'pain.001.001.04', 'pain.001.001.05', 'pain.001.001.06', 'pain.001.001.07', 'pain.001.001.08', 'pain.001.001.09', 'pain.001.001.10', 'pain.001.001.11', 'pain.001.001.12', 'pain.008.001.02' (see list_message_types).
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description adds value by detailing return format and emphasizing it checks shape only, aligning with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with purpose first, then usage context, then return format. The Args list is redundant given schema, but overall efficient.

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?

No output schema exists, but description fully specifies the return format. References to sibling tools and clear scope make it complete for a simple validation 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 coverage is 100% with descriptions for both parameters. The description's Args list largely restates schema content without adding new semantics, so baseline 3 is appropriate.

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 clearly states the tool validates flat records against a message type's JSON Schema. It distinguishes from sibling tools like validate_payment_scheme by specifying scope (JSON-Schema shape only).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises to use before generate_message and mentions when to use validate_payment_scheme for rulebook checks, providing clear when-to and when-not-to 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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