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Compare with commission apps

compare_commission_costs
Read-only

Works out what a takeaway or restaurant pays a percentage-commission delivery app (e.g. 30%) each month versus Deenwise's flat £1 per order plus a plan, and the saving.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "orders_per_month": "<orders_per_month>",
      "average_order": "<average_order>"
    },
    "name": "compare_commission_costs"
  }
}

Input Schema

NameRequiredDescriptionDefault
planNoDeenwise plan to compare, default Free
average_orderYesAverage order value in £
orders_per_monthYesOnline orders a month
current_commission_percentNoCurrent app commission %, default 30

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real substance beyond that by disclosing the computational model — flat £1 per order plus a plan versus a percentage commission — which tells the agent exactly what arithmetic the tool performs and that the output includes a saving figure.

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?

A single sentence that front-loads the calculation being performed and the two sides of the comparison, with no filler. Slightly dense, but every clause (commission side, flat-fee side, saving) carries information.

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?

There is no output schema, and the description does flag that a saving is returned, which is the key return-value hint. With four fully documented parameters and clear read-only annotations, the definition gives the agent enough to call it correctly, though it does not characterize the shape of the cost breakdown returned.

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%, with each parameter's meaning, units (£, %), bounds and defaults already documented in the schema. The description only echoes the 30% example and the £1 flat rate already implied by the schema defaults, so it adds no parsing or formatting guidance beyond it. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (works out) and a specific comparison resource: percentage-commission app fees versus Deenwise's flat £1 per order plus plan, including the resulting saving. It names the two cost models being contrasted, but never references a sibling such as deenwise_plans, so the boundary against other plan-related tools is left to the agent.

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 phrasing 'what a takeaway or restaurant pays ... versus Deenwise's' implies the use case (a restaurant evaluating switching), but there is no explicit when-to-use or when-not-to-use statement and no named alternative tool. Usage is inferable rather than stated.

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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