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netcafe-docs

by com.ainetcafe

Transaction CSV → QuickBooks .qbo bank feed file

csv_to_qbo
Read-onlyIdempotent

Convert a transaction CSV into a .qbo / OFX bank-feed file that QuickBooks and similar accounting software import directly. Needs date, description and amount columns (or debit + credit). Pairs with extract_statement: statement PDF in, importable bank feed out.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNoCSV content with a header row.
urlNoOr a link to the CSV.
bank_idNoRouting / bank identifier, if your import asks for one.
currencyNoThree-letter currency code, default USD.
account_idNoYour account number as the accounting software expects it.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint:false, so the safety profile is clear. The description adds valuable behavioral context by specifying input format requirements and the output's purpose (importable by QuickBooks). No contradiction with 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 two sentences, front-loaded with the core action, and every sentence contributes: first states the conversion, second gives input requirements and a sibling pairing. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With good annotations, an output schema, and 100% schema coverage, the description covers the essential purpose, input constraints, and relationship to extract_statement. A minor gap is not clarifying that either csv or url must be provided, but overall the context is sufficient for an AI agent to select and invoke the tool.

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 description coverage is 100%, so the baseline is 3. The description adds meaning by clarifying that the CSV must contain date, description, and amount (or debit+credit) columns, which directly informs how to use the 'csv' parameter. This goes beyond the schema's 'CSV content with a header row.'

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 a specific verb ('Convert'), a clear resource ('transaction CSV'), and a distinct output ('.qbo / OFX bank-feed file'), making the tool's purpose immediately obvious. It also distinguishes from siblings by explicitly pairing with extract_statement (PDF input to bank feed output).

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?

The description provides clear context by stating the required CSV columns ('date, description and amount columns (or debit + credit)') and mentions the complementary sibling tool extract_statement. However, it does not explicitly state when not to use this tool or list alternative tools for CSV conversion, so it stops short of a 5.

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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TDQS

A3.7/5.0
Disambiguation5/5

Each tool targets a unique operation—conversions, extractions, translations, and utilities like resume checking or redaction—with no meaningful overlap. The few similar tools (e.g., convert_to_pdf vs. xlsx_to_pdf) are clearly distinguished by input type.

Naming Consistency3/5

Naming mixes conventions: verb_noun (extract_tables, redact_text), noun_to_noun (xlsx_to_pdf, pptx_to_pdf), and unusual forms like doc_translate_cn and what_can_you_do. While snake_case is consistent, the verb/noun pattern is not, making the set slightly less predictable.

Tool Count3/5

With 23 tools, the server sits at the heavy end of the acceptable range. Every tool has a distinct purpose, but the spread across PDF handling, research, audio, and accounting utilities feels more like a miscellaneous collection than a focused suite, which could overwhelm agents.

Completeness4/5

The server covers a broad spectrum of document-processing tasks—conversion, extraction, translation, redaction, and validation—with few dead ends. Minor gaps exist (e.g., no PDF merge/split, no OCR for all scanned PDFs, no explicit delete/update for resources), but core workflows are well supported.

Resources