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Ricoledan

korral-storelink-mcp

by Ricoledan

List recent sales

korral_list_recent_sales
Read-onlyIdempotent

List individual POS transactions for a SKU, newest first, to determine if sales velocity comes from many small baskets or one large order, and to audit actual sales against projections.

Instructions

List individual POS transactions for a SKU at a store, most recent first. Use this to see whether a sales velocity figure from korral_check_stock_risk was driven by many small baskets or a single large one (e.g. a catering order), or as the audit trail when a projection needs to be double-checked against what actually sold.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skuYesSKU code to look up.
limitNoMax transactions to return. Hard cap 500.
sinceNoISO 8601 timestamp; only transactions at or after this time are returned. Defaults to 24 hours ago. Capped at 7 days back.
store_idNoWhich store. Optional if this deployment is authorized for exactly one store; required otherwise.
Behavior4/5

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

Annotations already establish safe read-only/idempotent behavior. The description adds concrete behavioral details: returns individual transaction lines (not aggregates), sorted most recent first, and serves as an audit trail. This goes beyond the schema's parameter descriptions. Minor gap: no mention of return fields or pagination, but schema covers limit.

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 primary action, and the second sentence provides valuable selection context without redundancy. Every word earns its place.

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 medium-complexity read-only tool with well-documented schema and clear annotations, the description provides purpose, granularity, ordering, and use-case guidance. The only missing piece is what fields appear in each transaction, but no output schema exists and the description's clarity compensates. It's sufficiently complete for an agent to select and invoke correctly.

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 baseline applies. The description reinforces that sku and store_id define the target, and context implies limit/since control the window, but it doesn't add new parameter-level meaning beyond the schema. It does tie 'sales velocity' to the sku parameter, which adds a hint, but not substantial.

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 uses a specific verb ('List') with a clear resource ('individual POS transactions') and scope ('for a SKU at a store, most recent first'). It also differentiates from sibling tools by referencing korral_check_stock_risk and framing this as the transactional detail behind that aggregate view.

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 states when to use: to break down sales velocity into basket sizes or as an audit trail against projections. Names the related sibling tool (korral_check_stock_risk) and gives a concrete decision scenario, providing clear guidance for tool selection.

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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