HelloBooks AI Agents

analyze_qbo_journal_cleanup

Scan a QuickBooks Online "Journal Entries" CSV export for cleanup issues — unbalanced journals (debits ≠ credits, with severity by deviation), duplicate journals (same date + same totals, likely posted twice), and schema problems (invalid dates, malformed amounts, missing accounts, missing journal numbers). Input is the raw CSV content the user pastes after exporting from QBO via Reports → Accountant → Journal → Export. Max 5,000 rows; max 5 MB. Returns a structured flag list with severity (high/medium/low), a roll-up summary by category and severity, parse diagnostics (column mapping + unmapped columns), and a shareable URL at agents.hellobooks.ai/r/{slug} (7-day TTL) that renders a branded analysis page suitable for sending to a CA or bookkeeper. Use this when a user pastes QBO journal data, asks "check my books", "find issues in my QBO journal", or "what is wrong with my journal entries". Each flag includes a fixableInHellobooks boolean — true means HelloBooks can resolve it automatically in the paid product.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`csvText`	Yes	Raw CSV text of a QuickBooks Online "Journal Entries" report. Export from QBO: Reports → Accountant → Journal → Export as CSV. Paste the file contents directly.
`fileName`	No	Optional original filename, used only as a label on the share page.

TDQS

A4.7/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses input limits, output structure, and shareable URL with TTL. However, it does not explicitly state the tool is read-only or has no side effects.

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?

Description is detailed and informative but somewhat long. Every sentence adds value, though it could be slightly more compact without losing clarity.

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?

Fully covers what the tool does, when to use it, input requirements, output breakdown (flags, summary, diagnostics, shareable URL), and limits (5000 rows, 5MB). No output schema, so description compensates well.

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

Parameters5/5

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

Both parameters are described in schema with 100% coverage. Description adds context: how to obtain the CSV (QBO export path), max size constraints, and the purpose of fileName as a label.

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 specifies the exact actions: scanning QBO journal CSV for unbalanced, duplicate, and schema issues. It clearly distinguishes from siblings by naming QBO cleanup and listing specific categories.

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: when user pastes QBO journal data and asks specific queries like 'check my books' or 'find issues'. Implicitly excludes Xero-related tools.

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

A3.9/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that specify the exact input and use case. Even similar tools like QBO vs Xero journal analyzers are differentiated by source system and specific checks, leaving no ambiguity.

Naming Consistency4/5

The majority of tools follow a verb_noun snake_case pattern (e.g., analyze_balance_sheet, list_features). A few exceptions like how_munimji_helps and feature_search deviate slightly, but overall the convention is consistent and readable.

Tool Count4/5

With 26 tools covering financial analysis, compliance, migration, and product info, the count is on the higher side but justified by the breadth of the accounting domain. Each tool serves a specific need without redundancy.

Completeness4/5

The tool set covers all major financial statement checks (balance sheet, P&L, trial balance, journal entries), compliance, migration, and product details. A notable gap is the lack of cash flow analysis, but the set is otherwise comprehensive for its scope.

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