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kartparash-cmd

Korral StoreLink MCP

Look up SKU

lookup_sku
Read-only

Resolve a product name fragment or SKU code to Korral StoreLink SKU records, returning exact SKU, category, and unit. Use this to obtain valid SKU identifiers before checking stock or creating orders.

Instructions

Resolve a product name fragment or SKU code to Korral StoreLink SKU records. Read-only.

WHAT IT RETURNS matches: an array of { sku, name, category, unit }.

  • sku Korral's 7-digit internal SKU code, e.g. "8847291". This is the only identifier the other tools in this server accept.

  • name Buyer-facing product name, e.g. "Madeta butter 250g".

  • category Merchandising category, e.g. "Dairy", "Bakery", "Produce".

  • unit The unit stock and sales are counted in: "each" or "kg". All quantities elsewhere in this server (on_hand, units_sold_last_24h, gap, order quantity) are expressed in this unit.

Matching is case-insensitive substring on name, plus exact or prefix match on sku. A query with no matches returns an EMPTY matches array and a hint — it is not an error. Re-query with a shorter or more distinctive fragment ("butter", not "Madeta butter 250g salted").

WHEN TO USE IT This is step 1 of the standard workflow and normally the FIRST call you make: lookup_sku -> get_stock_position -> create_replenishment_order -> get_replenishment_status Use it whenever the buyer names a product in words rather than digits ("how's the Madeta butter doing?", "check the sourdough"), whenever you are unsure a SKU code is valid, and whenever get_stock_position returns an unknown-SKU error. Multiple matches mean the query is ambiguous — show the buyer the candidates with their categories and units and ask which one, rather than picking for them.

WHEN NOT TO USE IT It returns no stock levels, no sales figures and no order history — call get_stock_position for that. It does not say which stores range a SKU — only get_stock_position reveals that, per store. It exposes nothing about suppliers (no vendor names, lead times, order minimums or costs), nothing about price, cost or margin, and no transaction-level POS data. Do not tell the buyer this server can retrieve any of those; it cannot, and no other tool here can either.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesA SKU code (exact or leading digits, e.g. "8847291" or "884") or a case-insensitive fragment of the product name (e.g. "butter", "sourdough"). Shorter fragments match more broadly; an empty result means try fewer words, not that the product is gone.
Behavior5/5

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

Beyond the readOnlyHint and openWorldHint annotations, the description discloses matching behavior (case-insensitive substring, prefix/exact SKU match), empty-match behavior (returns empty array and hint, not an error), and re-query guidance. It also explicitly lists what is not included (stock, sales, order history, supplier info). No contradiction with annotations.

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?

The description is long but efficiently structured with distinct sections (WHAT IT RETURNS, WHEN TO USE IT, WHEN NOT TO USE IT). Every sentence provides actionable information, with front-loaded purpose and clear formatting. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description carries the full burden of explaining return values. It details the matches array fields (sku, name, category, unit) with examples and explains the unit system. It also covers edge cases (no matches, ambiguous matches) and clearly states limitations relative to sibling tools. Complete for the tool's role.

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?

The schema already provides a detailed description of the query parameter, so baseline is 3. The description adds matching rules ('case-insensitive substring on name, plus exact or prefix match on sku') and clarifies the meaning of an empty result ('it is not an error'). This goes beyond the schema's parameter text, though schema coverage is already high (100%).

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 opens with a specific verb+resource: 'Resolve a product name fragment or SKU code to Korral StoreLink SKU records.' It clearly distinguishes itself from siblings by positioning it as 'step 1 of the standard workflow' and contrasting with get_stock_position which handles stock levels.

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?

The 'WHEN TO USE IT' and 'WHEN NOT TO USE IT' sections give explicit guidance: use as the first call, when buyers mention product names, or when get_stock_position returns an unknown-SKU error. It also explicitly names get_stock_position as the alternative for stock data, and explains what it cannot retrieve. This is exemplary usage guidance.

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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