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HelloBooks AI Agents MCP Server

by io.github.HelloBooksAIAgents

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
NameRequiredDescriptionDefault
csvTextYesRaw CSV text of a QuickBooks Online "Journal Entries" report. Export from QBO: Reports → Accountant → Journal → Export as CSV. Paste the file contents directly.
fileNameNoOptional original filename, used only as a label on the share page.

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses behavior: it performs read-only analysis, enforces limits (5k rows, 5MB), returns a shareable URL with TTL, and flags fixable items. No contradictions, and it adds context beyond the function name.

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 about 120 words, front-loaded with purpose, structured with bullet points for output components, and every sentence adds non-redundant information. The input repetition is minimal and justified.

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?

The description covers all major aspects: input format, limits, three analysis types, output structure (flags, summary, diagnostics, share URL), fixability indicator, and intended usage. No output schema exists, but the description compensates thoroughly.

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 both parameters documented, so baseline is 3. The description adds value by guiding the user on how to obtain the CSV (export path) and explains that fileName is just a label, making it a 4.

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 identifies the tool as a scanner for QBO Journal CSV exports, listing three specific issue categories (unbalanced, duplicates, schema problems) and distinguishing it from siblings like analyze_qbo_journal_anomalies by naming the platform and the exact export source.

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 states explicit triggers ('check my books', 'find issues') and provides the required input format. However, it does not explicitly advise against use in other scenarios or compare to alternative tools like analyze_qbo_journal_anomalies.

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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