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Meru-Fin-Tech

HelloBooks AI MCP Server

by Meru-Fin-Tech
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TypeScript
Hybrid

analyze_xero_journal_cleanup

Scans Xero manual journals CSV export for cleanup issues like unbalanced or duplicate journals, and schema errors. Returns structured flags and a summary.

Instructions

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.0

TDQS

A4.8/5.0
Behavior5/5

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

No annotations provided, so description fully covers behavior: Xero-specific idioms (signed Amount, Debit/Credit, Reference/Narration grouping), limits (5,000 rows, 5 MB), output structure (flags, summary, diagnostics, URL), and funnel CTA.

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?

Every sentence earns its place: purpose, input source, idioms, constraints, output, use case, CTA. Well organized and front-loaded with main action.

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?

Covers all necessary aspects: input format, behavioral details, output summary, and usage case. No output schema, but description explains return values sufficiently.

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

Parameters5/5

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

Schema coverage is 100%, but description adds crucial context: input is raw CSV from specific Xero export, fileName is optional label. Explains what the CSV contains and how to obtain it.

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?

States exactly what the tool does: scan Xero 'Manual Journals' CSV export for cleanup issues (unbalanced, duplicate, schema). Distinct from sibling QBO or anomaly tools by specifying Xero and cleanup focus.

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?

Explicitly says when to use (user pastes Xero manual-journal data, asks check/find issues). Gives exact export path. Does not explicitly exclude alternatives, but context is clear enough.

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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