Bella

Sales Summary

sales_summary

Read-onlyIdempotent

Summarizes revenue, orders, units, average order value, and margin percentage by month, category, region, or channel to track retail performance across a selected date range.

Instructions

Revenue, orders, units, average order value and margin %, grouped by month, category, region or channel.

Uses the official revenue definition (completed + shipped orders, net of discounts). Example: "how did revenue evolve this year?" -> group_by="month", start_date="2026-01-01".

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "sales_summary"
  }
}

Input Schema

Name	Required	Description	Default
`end_date`	No	Inclusive date, YYYY-MM-DD. Omit for no limit.
`group_by`	No		month
`start_date`	No	Inclusive date, YYYY-MM-DD. Omit for no limit.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Nov 3, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, closed-world and non-destructive, so safety is covered. The description adds real business context beyond that: the 'official revenue definition' of completed + shipped orders, net of discounts, which an agent could not infer from the schema. It omits any note on date-range defaults or result granularity, but the output schema covers return shape.

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?

Three compact lines: metrics first, then the revenue definition, then a concrete example. Every sentence carries distinct information and nothing is repeated from the schema.

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?

With an output schema present and date params documented in-schema, the definition covers what an agent needs: metrics, grouping options, and the revenue rule. A brief mention of the default grouping or whether results are always ordered by period would close the remaining gap.

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 67%; start_date/end_date already carry format and inclusivity docs, while group_by has no schema description. The description compensates by listing the grouping values and demonstrating start_date usage in the example, but adds no meaning for end_date or default behavior. This lands at the baseline-plus level.

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?

The description names the exact metrics returned (revenue, orders, units, AOV, margin %) and the four grouping axes, which is far more specific than the title 'Sales Summary'. It does not explicitly contrast itself with siblings like top_customers or ask_bella, but the metric-and-grouping framing makes its role unambiguous among them.

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 worked example ('how did revenue evolve this year?' -> group_by="month", start_date="2026-01-01") shows concretely when and how to call it. It stops short of stating exclusions, e.g. when to prefer ask_bella for free-form questions or top_customers for per-customer breakdowns.

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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