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Validate records against schema

validate_records
Read-onlyIdempotent

Validate flat records against a camt.05x message type's input JSON Schema to detect structural and type errors per row before XML generation.

Instructions

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

Use this on in-memory reversing-entry records to catch structural/type
errors per row before generation. To validate a whole camt.05x *document*
(XML) against its XSD instead, use ``validate_statement``.

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

Args:
    message_type: A supported ISO 20022 camt.05x message type.
    records: One or more flat reversing-entry records to validate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordsYesOne or more flat reversing-entry records (each a dict of field name to value) to validate row-by-row against the message type's input JSON Schema.
message_typeYesA supported ISO 20022 camt.05x message type string, e.g. 'camt.053.001.14', whose input JSON Schema the records are checked against. Call list_message_types to discover accepted values and get_input_schema to see the constraints.
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about the validation purpose (catch structural/type errors per row) and the return format, which aligns with and slightly extends the annotation hints.

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, well-structured description. The first line states purpose, followed by usage guidance, return format, and parameter details. Every sentence serves a purpose with 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?

Given the tool's simplicity (2 parameters, all required, no output schema), the description covers purpose, usage, return format, and parameter hints. It includes cross-references to related tools in parameter descriptions, making it complete for the agent.

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 description's Args section essentially repeats the schema descriptions without adding new meaning. Baseline score of 3 applies as no additional semantics are provided.

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 JSON schema, with a specific verb and resource. It distinguishes itself from the sibling 'validate_statement' by contrasting in-memory records vs XML documents.

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 states when to use (on in-memory reversing-entry records before generation) and when not to (for whole XML documents), and provides the alternative tool 'validate_statement'.

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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