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Validate records against schema

validate_records
Read-onlyIdempotent

Validate flat payment records against a message type's JSON Schema to catch structural errors before message generation. Returns a row-by-row error report with valid and total counts.

Instructions

Validate flat payment records against a message type's 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; to check a batch against a rail's usage guidelines use
``validate_scheme``.

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

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordsYesOne or more flat payment records, each a dict of field name -> value; validated against the message type's input JSON Schema (see get_input_schema / get_required_fields).
message_typeYesA supported ISO 20022 pacs message type, e.g. 'pacs.008.001.08' FI-to-FI Customer Credit Transfer. Must be exactly one of: 'pacs.002.001.12', 'pacs.003.001.09', 'pacs.004.001.11', 'pacs.007.001.11', 'pacs.008.001.01', 'pacs.008.001.02', 'pacs.008.001.03', 'pacs.008.001.04', 'pacs.008.001.05', 'pacs.008.001.06', 'pacs.008.001.07', 'pacs.008.001.08', 'pacs.008.001.09', 'pacs.008.001.10', 'pacs.008.001.11', 'pacs.008.001.12', 'pacs.008.001.13', 'pacs.009.001.10', 'pacs.010.001.05', 'pacs.028.001.05' (see list_message_types).
Behavior4/5

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

Description adds row-by-row error report and scope (JSON-Schema shape only) beyond annotations (readOnly, idempotent). No contradiction; transparency is strong.

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?

Concise two-paragraph structure with front-loaded purpose and bullet for return format. Every sentence adds value.

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?

With full schema coverage, annotations, and clear return format description, the tool is fully specified for correct invocation.

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%, so baseline 3. Description repeats parameter info from schema but adds no significant new meaning; minimal added value.

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?

Description clearly states it validates flat payment records against a message type's JSON Schema. The verb 'validate' and resource 'records' are specific. It distinguishes from siblings like generate_message and validate_scheme.

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 using this before generate_message and mentions validate_scheme as alternative for rail-level checks. Provides clear context for when to use.

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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