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HelloBooks AI Agents MCP Server

by io.github.HelloBooksAIAgents

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
NameRequiredDescriptionDefault
areaNoOptional. Narrow to one business-operation area.
autonomyNoOptional. 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).
businessDescriptionNoOptional. 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

A4.6/5.0
Behavior4/5

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

With no annotations, the description adds important behavioral context: the AI never posts to a ledger without approval, and it explains autonomy levels (autonomous, approval, assist, manual). This goes beyond the schema, though it does not detail side effects or rate limits, which are less relevant for this knowledge retrieval tool.

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 long but well-structured: purpose first, then output details, usage examples, parameter guidance, and sibling tool references. It front-loads key information and avoids fluff, earning its length.

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?

Since there is no output schema, the description fully explains the return structure (areas, autonomy levels, links to features) and provides context on autonomy definitions and usage. It also references sibling tools for completeness.

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%, so baseline is 3. The description adds value by explaining how businessDescription is used (echoed back, LLM matching, no keyword scoring) and giving concrete example queries. It also elaborates on the autonomy enum values, exceeding the baseline.

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, returning a curated capability knowledge base with areas and autonomy levels. It distinguishes from siblings like list_features (full catalog) and list_plans (pricing) by referencing them.

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 gives example queries for when to use this tool (e.g., 'how can HelloBooks help me?', 'what can Munimji do on its own vs approve?') and directs users to list_features for full catalog and list_plans for pricing, 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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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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