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HelloBooks AI Agents MCP Server

by io.github.HelloBooksAIAgents

analyze_balance_sheet

Take a Balance Sheet CSV export from QuickBooks Online, Xero, Zoho Books, or Wave (source auto-detected) and run three checks: (1) bs.equation_broken — the fundamental accounting equation Assets = Liabilities + Equity does not hold (every downstream ratio analysis is invalid until fixed); (2) bs.negative_asset — Cash / AR / Inventory line items with negative balances (reconciliation error signal); (3) bs.negative_equity — Total Equity < 0 (insolvency signal). Input is raw CSV text of a Balance Sheet (Reports → Balance Sheet in QBO / Xero / Zoho / Wave). Max 5,000 rows; max 5 MB. Returns flags with severity, totals (totalAssets, totalLiabilities, totalEquity, equationBalances boolean), and a shareable URL. Use this when a user pastes a Balance Sheet and asks "does my balance sheet balance?", "is the accounting equation satisfied?", or "is my company solvent on paper?". A Balance Sheet that fails Assets = Liabilities + Equity invalidates every downstream financial-ratio analysis — this is the single most important check for any BS.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvTextYesRaw CSV text of a Balance Sheet report. Works with QuickBooks Online (Reports → Balance Sheet), Xero (Reports → Balance Sheet), Zoho Books (Reports → Balance Sheet), and Wave (Reports → Balance Sheet). Statement should include Total Assets, Total Liabilities, and Total Equity rows. Source is auto-detected from section name signatures.
fileNameNoOptional filename for the share-page label.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It details the three checks performed, the input limits (5k rows, 5 MB), and the output (flags, totals, equationBalances boolean, shareable URL). It does not mention non-destructive nature or auth needs, but behavior is well explained.

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 front-loaded with the core purpose and checks. Every sentence adds value: input format, limits, output, usage guidance. There is no redundant or irrelevant information, making it efficient and clear.

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?

Given two parameters and no output schema, the description covers input requirements, processing logic, and output summary well. It provides interpretation of results and usage context. Minor gap: no detailed output structure, but sufficient for an agent to use 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 coverage is 100% with parameter descriptions. The description adds valuable context about supported sources (QuickBooks, Xero, Zoho, Wave) and auto-detection, and explains the role of the optional fileName. This goes beyond the schema, earning a score above baseline 3.

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 takes a Balance Sheet CSV from specific sources and runs three well-defined checks. It specifies the verb 'analyze' and the resource 'balance sheet', and distinguishes from siblings by emphasizing this is the fundamental check before ratio analysis.

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 gives explicit usage scenarios: 'when a user pastes a Balance Sheet and asks [specific questions]'. It explains why the tool is important and mentions limits. It does not explicitly say when not to use it or name alternatives, but 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

A3.8/5.0
Disambiguation4/5

Tools are largely distinct, each targeting a specific report type (balance sheet, P&L, trial balance) or functionality. Some overlap exists between QBO and Xero variants, but descriptions clearly differentiate them. Overall, an agent can reliably select the right tool.

Naming Consistency4/5

Most tools follow a consistent snake_case verb_noun pattern (e.g., analyze_balance_sheet, list_articles). A few exceptions like free_tier_eligibility and how_munimji_helps break the pattern but are still readable and predictable.

Tool Count3/5

With 29 tools, the count is on the higher side but justified by the domain's breadth (financial analysis, compliance, migration, pricing, etc.). Some reduction through parameterization (e.g., merging QBO/Xero variants) would improve scope.

Completeness4/5

The tool surface covers key areas: financial statement analysis, compliance, migration estimation, feature/pricing info, and partner programs. Missing are direct data manipulation tools (e.g., create/edit journal entries), which may be intentional for a read-only analysis agent.

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