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Validate records against schema

validate_records
Read-onlyIdempotent

Validate flat account records against a message type's JSON Schema to catch structural and type errors per record, returning a row-by-row error report.

Instructions

Validate flat account 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; to validate a single financial identifier in isolation use
``validate_identifier``.

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

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordsYesOne or more flat account 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 acmt message type, e.g. 'acmt.001.001.08' Account Opening Instruction -- call list_message_types for the exact accepted strings.
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. Description adds that it checks JSON-Schema shape only and returns a row-by-row error report with specific fields. No contradiction.

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?

Extremely concise, front-loaded with purpose, then usage, return format, and parameter details. No wasted words.

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 2-param tool with no output schema, description fully explains what it validates, what it returns, and references siblings. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%. Description adds meaningful context: records are 'flat account records', message_type gives examples and refers to list_message_types. Adds value beyond schema descriptions.

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?

Clearly states it validates flat account records against a message type's input JSON Schema. Distinguishes from siblings by specifying use before generate_message and contrasting with validate_identifier for single identifiers.

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 says when to use (before generate_message to catch errors) and when not (use validate_identifier for single identifier validation). References sibling tools like list_message_types.

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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