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My sales statistics

shop.merchant.stats
Read-only

Your own sales statistics (needs stats:read), the test SKU excluded: quotes -> paid -> closed, gross/net/fee, average order, refunds, disputes, top-20 products, by rail, by agent (client name/version, user-agent family, 8-char wallet hash prefix), webhook success % and p95, overdue SLA counts, the Base fee receivable (owed, invoiced, open fee invoices). Optional from/to (ISO 8601, default last 30 days, at most 366), group day|week. format=csv returns one row per paid order with tx_hash (payer as hash prefix only). Cached for 60 s.

Input Schema

NameRequiredDescriptionDefault
toNo
fromNo
groupNo
formatNo

Output Schema

NameRequiredDescriptionDefault
toNo
csvNo
fromNo
groupNo
funnelNo
seriesNo
by_railNo
fee_usdNo
net_usdNo
refundsNo
webhookNo
by_agentNo
disputesNo
gross_usdNo
sla_overdueNo
top_productsNo
avg_order_usdNo
fee_receivableNo
subscriptions_activeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / properties / subscriptions_active
      Added value: +{
      +  "type": "number"
      +}
  2. Changed1 schema field changed
    • addedOutput schema / properties / fee_receivable
      Added value: +{
      +  "additionalProperties": {},
      +  "type": "object"
      +}
  3. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover readOnly/non-destructive/closed-world, so the bar is lower. The description still adds useful context beyond them: the 'stats:read' permission requirement, exclusion of the test SKU, and a 60-second cache window.

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

Conciseness3/5

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

The scope statement is front-loaded, but the bulk is a single long comma-delimited run-on list of metrics that could be trimmed or grouped. Functional but not tight.

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 complex stats tool with an output schema, the description covers scope, permissions, caching, and parameter defaults. Since a return schema exists, it appropriately omits field-level return documentation.

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 0%, so the description must carry parameter meaning. It does compensate well: from/to are ISO 8601 with a 30-day default and 366 cap, group is day|week, and format=csv changes the return shape (one row per paid order with hashed tx_hash).

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+resource ('Your own sales statistics') and enumerates the metrics returned (quotes->paid->closed, gross/net/fee, refunds, disputes, etc.). Clear enough to know what it produces, though there are no close siblings requiring explicit differentiation.

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?

Implies usage context by stating 'Your own sales statistics (needs stats:read)' and documenting defaults for from/to. However, it gives no explicit when-to-use vs when-not-to-use guidance or named alternatives.

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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