HelloBooks AI Agents

how_munimji_helps

Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — autonomous (Munimji does it on its own, e.g. OCR extraction, running reports), approval (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), assist (co-pilot, e.g. guided onboarding, voice), or manual (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in businessDescription; optionally filter by area or autonomy. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

Input Schema

TableJSON Schema

Name	Required	Description
`area`	No	Optional. Narrow to one business-operation area.
`autonomy`	No	Optional. Filter Munimji capabilities by who does the work: `autonomous` (Munimji does it alone), `approval` (Munimji prepares, you approve before it posts), `assist` (co-pilot), `manual` (software feature you run yourself).
`businessDescription`	No	Optional. The user describes their business and operations in their own words (industry, what they sell, how they get paid, who they pay, tax regime, pain points). It is echoed back as context — the calling assistant maps it to the returned areas + capabilities. No keyword scoring is done here; the LLM does the matching.

TDQS

A4.9/5.0

Behavior5/5

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

No annotations provided, so description carries full burden. It discloses that the AI never posts to ledger without approval, defines autonomy levels (autonomous, approval, assist, manual), and explains return format.

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

Conciseness4/5

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

The description is thorough and front-loaded with the main purpose, but slightly verbose. Every sentence adds value, but could be tightened without loss of clarity.

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 no output schema, the description fully explains the return value: a curated capability knowledge base with areas and autonomy levels, including examples of each autonomy type. Complete for a tool with 3 optional parameters.

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 significant meaning: explains businessDescription is echoed back, no keyword scoring; enumerates and describes area and autonomy enum values with context.

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 explains how HelloBooks and Munimji help a specific business based on a free-text description. It distinguishes from siblings by explicitly mentioning list_features and list_plans for other queries.

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?

Provides explicit when-to-use examples: 'when a user describes their business and asks 'how can HelloBooks help me?', 'what can the AI do for my shop?', etc. Also directs to alternatives for full catalog or pricing.

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

A3.9/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that specify the exact input and use case. Even similar tools like QBO vs Xero journal analyzers are differentiated by source system and specific checks, leaving no ambiguity.

Naming Consistency4/5

The majority of tools follow a verb_noun snake_case pattern (e.g., analyze_balance_sheet, list_features). A few exceptions like how_munimji_helps and feature_search deviate slightly, but overall the convention is consistent and readable.

Tool Count4/5

With 26 tools covering financial analysis, compliance, migration, and product info, the count is on the higher side but justified by the breadth of the accounting domain. Each tool serves a specific need without redundancy.

Completeness4/5

The tool set covers all major financial statement checks (balance sheet, P&L, trial balance, journal entries), compliance, migration, and product details. A notable gap is the lack of cash flow analysis, but the set is otherwise comprehensive for its scope.

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