HelloBooks AI Agents

compare_books_to_hellobooks

Take a QBO or Xero journal-entry CSV (source auto-detected), run the full Tier-0 detection set (imbalance + duplicates + round-number + schema), and return a structured side-by-side comparison — "your books have X issues; here is how HelloBooks resolves each phase". This is the direct funnel tool: the response includes per-category counts mapped to HelloBooks Phases 1, 2, 3.0, 3.1, with exclusive-advantage bullets (command-center dashboard, conversational interface, one-prompt JE posting, cross-phase orchestration, auto ID resolution). Use this when a user is evaluating HelloBooks vs their current QBO/Xero, asks "should I migrate?", or pastes data while comparing accounting software. Output is suitable for the host LLM to narrate as a positioning argument; the share URL points at a branded landing page with the issue breakdown and a 1-click migrate CTA.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`csvText`	Yes	Raw CSV text of a journal-entry export from QBO ("Journal Entries") or Xero ("Manual Journals"). Source is auto-detected from the headers.
`fileName`	No	Optional filename label.

TDQS

A4.4/5.0

Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool auto-detects the source, runs multiple detection sets, and returns a structured comparison with per-category counts and advantage bullets. It does not mention side effects, but the tool appears to be a read-only analysis. Minor gap: no explicit statement of non-destructiveness or error handling for invalid input.

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?

The description is well-structured with three sentences: core function, use case, and output/appropriateness. It is front-loaded with the action. While somewhat long, each sentence adds distinct value, making it efficient.

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?

Despite no output schema, the description thoroughly explains the output structure (per-category counts, phases, exclusive-advantage bullets, share URL). It covers input, processing, output, and use case, providing complete context for an agent to select and invoke the tool correctly.

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% with clear parameter descriptions for csvText and fileName. The tool description adds value by explaining the purpose of the CSV, auto-detection, and the mapping of results to HelloBooks phases, which goes beyond the schema.

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's purpose: taking a QBO or Xero journal-entry CSV, running a detection set (imbalance, duplicates, etc.), and returning a structured side-by-side comparison. It specifies the output includes per-category counts mapped to HelloBooks phases and exclusive-advantage bullets, distinguishing it from sibling analysis tools.

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 explicitly says when to use: when a user is evaluating HelloBooks vs QBO/Xero, asks about migration, or pastes data for comparison. It notes the output is suitable for the host LLM to narrate as a positioning argument. However, it lacks explicit when-not-to-use guidance, though the context is clear.

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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