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OTA commission savings calculator

calculate_ota_commission_savings
Read-only

Call this to quantify what a hotel pays OTAs per year and what shifting 30% of those bookings to commission-free direct bookings would recover. Same formula as frontdesko.app/ota-commission-calculator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
adrYesAverage daily rate (per room-night)
roomsYesNumber of rooms
currencyNoISO currency code for display (default USD)
occupancy_pctYesOccupancy percentage
ota_share_pctYesShare of bookings arriving via OTAs, %
commission_pctYesAverage OTA commission rate, %

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds the 30% shift assumption and formula reference, but doesn't disclose output shape or calculation caveats.

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?

Two sentences, front-loaded with the action and outcome; the formula reference is the only extra and it is useful for provenance.

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?

For a read-only calculator with fully documented parameters, the description conveys the computation and expected result. It doesn't specify return format, but the outcome is implied by 'quantify...recover'.

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 coverage is 100%, so the schema documents all parameters. The description adds no parameter-level detail beyond the 30% scenario, which is not a parameter.

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 a specific verb ('quantify') and resource (hotel OTA payments and savings from shifting 30% of bookings), clearly distinguishing it from sibling tools like compare_pms or recommend_software.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear invocation purpose but does not state when to prefer it over alternatives or mention any exclusions. Usage context is implied by the calculator scope rather than explicit.

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
Disambiguation4/5

Most tools target clearly distinct jobs—calculating savings, checking availability, comparing competitors, pricing, recommending, and searching docs. The broad search_frontdesko_docs tool can overlap with the other informational tools, but the specific descriptions make the intended choice clear.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: calculate_, check_, compare_, get_, recommend_, search_. There are no mixed conventions or vague verbs, making the toolset predictable and easy to scan.

Tool Count5/5

Six tools is well-scoped for a hotel-software sales/support assistant. Each tool covers a distinct user need and none feel redundant with the others.

Completeness4/5

The set covers pricing, competitor comparison, documentation, live demo availability, ROI calculation, and recommendations, which are the core front-of-funnel questions. It lacks a booking or contact-sales action, but that may be intentional for a read-only informational server.