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Meru-Fin-Tech

HelloBooks AI MCP Server

analyze_xero_journal_anomalies

Scan Xero manual journal CSV exports to detect anomalous round-number lines (debit or credit multiples of $1,000) and flag them with severity levels based on amount.

Instructions

Scan a Xero "Manual Journals" CSV export for anomalies — currently round-number lines (debit or credit amounts that are exact multiples of $1,000, above a $1,000 materiality threshold). Input is raw CSV text from Xero Accounting → Advanced → Manual Journals → Export. Max 5,000 rows; max 5 MB. Returns flagged lines with severity ($100K+ high, $10K+ medium, else low) and a shareable URL. Use this when a user pastes Xero data and asks "any anomalies?", "look for round numbers", or "anything suspicious". Same Tier-0 / paid-product split as the QBO variant — history-aware anomaly checks (GL outliers, vendor history, archived-vendor activity, LLM-narrated suspicious) live in the authenticated MCP / paid product.

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.
Behavior5/5

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

Discloses input limits (max 5,000 rows, 5 MB), output structure (flagged lines with severity and URL), and that it is not history-aware (advanced checks in paid product). With no annotations, description fully shoulders transparency burden.

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?

Packed with essential information in a structured flow: purpose, input, constraints, output, usage scenarios, and differentiation. No wasted sentences.

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?

Comprehensive coverage given no output schema or annotations: explains input format, output details, constraints, usage context, and limitations (only round numbers, not history-aware). Completeness is high.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema provides full descriptions for both parameters (100% coverage). Description adds constraints (max rows/size) not in schema, but no other substantive new meaning beyond 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?

Clearly states it scans Xero Manual Journals CSV for round-number anomalies, specifying input source, what anomalies it finds, and output (flagged lines with severity and shareable URL). Distinguishes from sibling tools by mentioning QBO variant and paid product for advanced checks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this when a user pastes Xero data and asks any anomalies?, look for round numbers, or anything suspicious.' Contrasts with paid product for deep analysis, providing clear when-to-use and when-not-to-use guidance.

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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