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Meru-Fin-Tech

HelloBooks AI MCP Server

analyze_journal_variance

Compare two periods of journal entries (QBO or Xero) and flag accounts where net movement deviates materially between periods, with optional period labels for clarity.

Instructions

Compare two periods of journal-entry data (QBO or Xero — source auto-detected from headers) and flag accounts whose movement deviates materially between periods. Aggregates lines per account into a net total for each period, then surfaces accounts where the period-over-period change crosses a materiality threshold (≥5% relative AND ≥$100 absolute; severity high at ≥50%, medium at ≥20%, low at ≥5%). Inputs are two CSV exports — periodACsv (earlier period) and periodBCsv (later period). Optional periodALabel / periodBLabel for human-readable flag messages (e.g. "Q1 FY2024" vs "Q2 FY2024"). Max 5,000 rows per period; max 5 MB each. Use this when a user pastes two periods and asks "what changed?", "show me variances", "what jumped period-over-period". Returns a flag list ordered by largest delta, a roll-up, and a shareable URL. Both periods must be the same source — mixing QBO + Xero in one call returns an error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodACsvYesRaw CSV text of the EARLIER period's journal-entry export (QBO Journal Entries or Xero Manual Journals). Source is auto-detected from the headers.
periodBCsvYesRaw CSV text of the LATER period's journal-entry export. Source is auto-detected from the headers (must match periodACsv).
periodALabelNoOptional human label for the earlier period — e.g. "Q1 FY2024". Used in flag messages.
periodBLabelNoOptional human label for the later period — e.g. "Q2 FY2024".
Behavior5/5

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

No annotations provided, so the description fully discloses behavior: auto-detection of source, aggregation logic, materiality thresholds (≥5% relative AND ≥$100 absolute with severity levels), input limits (5,000 rows, 5 MB), output structure (flag list, roll-up, shareable URL), and error condition for mixed sources.

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?

Description is well-structured and concise, with no fluff. It front-loads the purpose, then details algorithm, inputs, usage, and output in logical order.

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 tool's complexity (multiple inputs, thresholds, output), the description is comprehensive. It covers behavior, constraints, usage, and error conditions without requiring an output schema.

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 description coverage is 100%; the description adds context (e.g., periodACsv is earlier, labels appear in flag messages) but does not significantly expand beyond the schema 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 verb 'compare', resource 'journal-entry data', and outcome 'flag accounts whose movement deviates materially between periods'. It distinguishes from sibling tools like analyze_qbo_journal_anomalies by focusing on period-over-period comparison rather than intra-period 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?

Explicit usage cues are provided: 'when a user pastes two periods and asks "what changed?", "show me variances", "what jumped period-over-period"'. It also indicates constraints (max rows, same source) but does not explicitly name alternative tools for different scenarios.

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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