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sebastienrousseau

bankstatementparser-mcp

Validate statement (dry run)

validate_statement
Read-onlyIdempotent

Validates a bank statement text to confirm it is well-formed and parseable. Returns pass/fail status and transaction count without extracting data.

Instructions

Dry-run parse an inline statement to check it parses cleanly.

Use this to confirm a payload is well-formed and parseable before
committing to a full read; it returns a structured pass/fail with the
transaction count but never the rows themselves, and never raises on a
parse error. To actually read the transactions use ``parse_statement``.

Args:
    content: The raw statement text.
    filename: Original filename; its extension selects the format.
    format: Explicit format override.

Returns:
    A dict with ``is_valid``, the resolved ``format``, the
    ``transaction_count`` on success, and an ``error`` on failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoExplicit format identifier that overrides detection from the filename. One of: 'camt' (CAMT.053), 'pain001', 'csv', 'ofx', 'qfx', 'mt940'. When omitted, the format is inferred from the filename extension.
contentYesThe raw statement text to validate, inline (not a file path). Accepts ISO 20022 CAMT.053 and pain.001 XML, SWIFT MT940, CSV exports, and OFX/QFX payloads.
filenameNoOriginal filename of the payload; its extension (.xml, .csv, .ofx, .qfx, .mt940, .sta) selects the format when 'format' is omitted. Defaults to 'statement.xml'.statement.xml

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already indicate read-only and idempotent behavior. The description adds that it returns structured pass/fail, never rows, and never raises on parse error, which aligns with and augments the annotations.

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?

The description is concise with a clear purpose statement, usage guidance, and structured Args/Returns sections. 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?

Given the tool's complexity (3 params, output schema, annotations), the description covers all necessary aspects: purpose, usage, parameters, return structure, and behavioral notes. 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%, so the description is not required to repeat parameter details. However, it adds value by listing explicit format enum values ('camt', 'pain001', etc.) not present in the schema, improving semantics.

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 states 'Dry-run parse an inline statement to check it parses cleanly,' which is a specific verb and resource. It also contrasts with the sibling 'parse_statement,' making the purpose unambiguous.

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 committing to a full read' and directs to 'parse_statement' for actual reading. It also clarifies error behavior, 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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