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PivotBank

Convert a bank statement PDF to Excel, CSV, QuickBooks or Xero

convert_statement

Use this when the user wants a bank statement or credit card statement PDF turned into a spreadsheet: Excel (xlsx), CSV, QuickBooks (QBO), Xero (OFX), Quicken (QIF) or JSON. Reads every transaction (date, description, money in, money out, balance) and checks them against the statement's own running balances, so the reading is proved rather than guessed. Saves the statement to the user's PivotBank account and returns a link to download it in any of those formats. Needs the user to sign in to PivotBank; agents may instead send a Pro API key as 'Authorization: Bearer pvb_live_...'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameNoThe file name, for your own records.
statementNoThe bank statement PDF the user shared.
pdf_base64NoFor clients without file attachments: the statement PDF, base64-encoded. 15 MB before encoding at most.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only declare the safety profile (non-readonly, non-destructive, non-idempotent, closed-world). The description adds substantive behavior beyond them: it discloses the verification step against running balances, the side effect of saving the statement to the user's account, the returned download link, and two authentication paths (user sign-in or a Bearer API key). That is exactly the extra context an agent needs.

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?

Three sentences, front-loaded with the trigger, then behavior, then auth. Every sentence carries information, though the format list is repeated twice (once as output formats, once as 'any of those formats'), which is mild redundancy.

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 no output schema, the description covers the return value (a download link usable in any listed format), the auth requirements, the verification behavior, and the storage side effect. An agent has everything needed to invoke it correctly and set user expectations.

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 description coverage is 100% and all three parameters (filename, statement nested object, pdf_base64) are documented in the schema, including the 15 MB base64 limit and the 'clients without attachments' case. The description adds no parameter-level detail beyond that, so the baseline 3 applies.

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?

States a specific verb+resource (convert a bank/credit card statement PDF) and enumerates the exact output formats (xlsx, CSV, QBO, OFX, QIF, JSON). This is clearly distinguishable from siblings like check_statement_balances and summarise_spending, which operate on the statement rather than convert it.

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

Usage Guidelines4/5

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

Opens with an explicit trigger: 'Use this when the user wants a bank statement or credit card statement PDF turned into a spreadsheet.' That gives a clear when-to-use context, but it never names an alternative tool or states when NOT to use this one, so routing between it and the balance-checking sibling is left to inference.

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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