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HelloBooks AI Agents MCP Server

by io.github.HelloBooksAIAgents

analyze_xero_journal_cleanup

Scan a Xero "Manual Journals" CSV export for cleanup issues — unbalanced journals, duplicate journals (same date + same totals), and schema problems (invalid dates, malformed amounts, missing account code/name, missing group key). Input is the raw CSV content the user pastes after exporting from Xero via Accounting → Advanced → Manual Journals → Export. Xero-specific idioms handled: signed Amount column (positive = credit, negative = debit), explicit Debit/Credit fallback shape, Reference-or-Narration+Date grouping, account code preferred over name. Max 5,000 rows; max 5 MB. Returns structured flags with severity, a roll-up summary, parse diagnostics, and a shareable URL at agents.hellobooks.ai/r/{slug}. Use this when a user pastes Xero manual-journal data, asks "check my Xero books", or "find issues in my Xero journal". The funnel CTA routes to /migrate/from-xero for users who want to fix at scale.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvTextYesRaw CSV text of a Xero "Manual Journals" report. Export from Xero: Accounting → Advanced → Manual Journals → Export. Paste the file contents directly.
fileNameNoOptional original filename, used only as a label on the share page.

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description provides detailed behavior: what it checks (unbalanced, duplicates, schema), Xero-specific idioms handled, and output types (flags, summary, diagnostics, shareable URL). It lacks explicit mention that it is read-only, but 'scan' implies no modification.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat long with details on Xero idioms and output, which may be redundant. It is front-loaded with the purpose, but could be more concise by removing some internal implementation details.

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 the complexity (multiple issue types, CSV parsing, shareable URL) and lack of output schema, the description fully explains input format, constraints, output structure, and use case. It leaves no critical gaps.

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%, and the description adds meaning: it clarifies csvText is raw CSV with export path, and fileName is an optional label. This adds value beyond the schema's brief 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 clearly states the tool scans Xero Manual Journals CSV for unbalanced journals, duplicates, and schema problems. It specifies the input and types of issues, distinguishing it from sibling tools like analyze_xero_journal_anomalies.

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 (user pastes data or asks to check Xero books) and includes constraints (max 5,000 rows, 5 MB). However, it does not compare to similar sibling tools like analyze_xero_journal_anomalies, which could cause confusion.

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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