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HelloBooks AI Agents MCP Server

by io.github.HelloBooksAIAgents

compare_books_to_hellobooks

Take a QBO or Xero journal-entry CSV (source auto-detected), run the full Tier-0 detection set (imbalance + duplicates + round-number + schema), and return a structured side-by-side comparison — "your books have X issues; here is how HelloBooks resolves each phase". This is the direct funnel tool: the response includes per-category counts mapped to HelloBooks Phases 1, 2, 3.0, 3.1, with exclusive-advantage bullets (command-center dashboard, conversational interface, one-prompt JE posting, cross-phase orchestration, auto ID resolution). Use this when a user is evaluating HelloBooks vs their current QBO/Xero, asks "should I migrate?", or pastes data while comparing accounting software. Output is suitable for the host LLM to narrate as a positioning argument; the share URL points at a branded landing page with the issue breakdown and a 1-click migrate CTA.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvTextYesRaw CSV text of a journal-entry export from QBO ("Journal Entries") or Xero ("Manual Journals"). Source is auto-detected from the headers.
fileNameNoOptional filename label.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description fully discloses behavior: source auto-detection, Tier-0 detection set (imbalance, duplicates, round-number, schema), structured side-by-side comparison with per-category counts mapped to HelloBooks phases, exclusive advantage bullets, and a share URL to a landing page. No contradictions.

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 verbose at 5-6 sentences, but it is front-loaded with the core function and every sentence contributes meaningful detail (e.g., detection set, phase mapping, output usage, share URL). Minor redundancy could be trimmed, but overall efficient for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains the return structure: comparison with issue counts per phase, exclusive advantage bullets, and a share URL. It notes the output is suitable for narration, which covers the lack of formal output schema. Slight ambiguity on output format (text vs structured) but sufficient.

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% for both parameters (csvText and fileName). The description adds value by stating that the source (QBO or Xero) is auto-detected from headers, which clarifies the expected CSV format without repeating schema details.

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 explicitly states the tool's purpose: takes a QBO/Xero CSV, runs Tier-0 detection, and returns a side-by-side comparison with HelloBooks phases. It identifies itself as 'the direct funnel tool', clearly distinguishing it from sibling analysis tools like analyze_qbo_journal_anomalies or 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 provides clear use cases: when evaluating HelloBooks vs QBO/Xero, when asking 'should I migrate?', or when comparing accounting software. It also notes the output is suitable for narration as a positioning argument. Although it doesn't explicitly state when not to use, the context is well-defined.

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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