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HelloBooks AI Agents MCP Server

by io.github.HelloBooksAIAgents

analyze_trial_balance

Take a Trial Balance CSV export from QuickBooks Online, Xero, Zoho Books, or Wave (source auto-detected from headers — YTD columns indicate Xero, Opening Balance indicates Zoho, etc.) and run three checks: (1) tb.unbalanced — debits ≠ credits (every downstream P&L / BS / cash-flow report built from this TB is wrong until fixed); (2) tb.wrong_sign — accounts whose name suggests a class (Revenue / COGS / Expense / AR / AP) carrying a balance on the wrong side (classic posting-error signal); (3) tb.round_balance — exact-multiple-of-$10,000 balances (plug-entry signal). Input is raw CSV text of a Trial Balance report. Max 5,000 rows; max 5 MB. Returns flagged accounts with severity, a roll-up showing whether the TB balances, parse diagnostics, and a shareable URL at agents.hellobooks.ai/r/{slug}. Use this when a user pastes a Trial Balance and asks "does my TB balance?", "are there sign errors?", "what looks suspicious?", or "is this TB clean?". The Trial Balance is the foundation document for every other financial statement — if it does not balance, every downstream report is invalid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvTextYesRaw CSV text of a Trial Balance report. Works with QuickBooks Online (Reports → Trial Balance), Xero (Reports → Trial Balance), Zoho Books (Reports → Accountant → Trial Balance), and Wave (Reports → Trial Balance). Source is auto-detected from column headers.
fileNameNoOptional filename for the share-page label.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description fully covers behavioral traits: it auto-detects source, runs three distinct checks, has row and size limits (5,000 rows, 5 MB), and returns specific outputs (flagged accounts, severity, roll-up, diagnostics, shareable URL). No contradictions.

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 somewhat lengthy but well-organized: it begins with the core action, lists checks, input requirements, outputs, and usage scenarios. Each sentence adds value, and it is front-loaded for 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?

Given no output schema, the description sufficiently explains return values (flagged accounts, roll-up, diagnostics, URL). It covers all key aspects of the tool's functionality, including limits and the importance of the trial balance for downstream reports.

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%, but the description adds valuable context by detailing supported sources, auto-detection from headers, and the purpose of the optional filename. This goes beyond the schema's property descriptions.

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 explicitly states the tool takes a Trial Balance CSV and runs three specific checks (unbalanced, wrong sign, round balance). It distinguishes from sibling tools like analyze_balance_sheet and analyze_profit_loss by focusing solely on the trial balance, which is foundational for other reports.

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 use cases (e.g., 'does my TB balance?', 'are there sign errors?') and explains that the trial balance is the foundation for downstream reports. While it doesn't explicitly list when not to use, the context implicitly directs users to this tool when dealing with trial balance data.

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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