retail-ops-mcp-kit

get_goods_receipt

Fetch goods receipt records for a purchase order to verify actual received quantities and identify discrepancies such as short-ship, over-ship, or damaged items.

Instructions

Fetch the goods receipt(s) for a PO — the GRN "actual" leg of the match.

- po_id: corpus PO id, e.g. "PO-8801".

Under sap_s4hana returns an API_MATERIAL_DOCUMENT_SRV A_MaterialDocumentHeader
collection with a to_MaterialDocumentItem list (GoodsMovementType 101). `condition`
and `qty_damaged` from the corpus surface as item-level annotations (short-ship /
over-ship / damage / UOM-mismatch traps). Returns an empty collection if nothing was
received.

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": {
      "po_id": "<po_id>"
    },
    "name": "get_goods_receipt"
  }
}

Input Schema

Name	Required	Description	Default
`po_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 11, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses the return structure (A_MaterialDocumentHeader collection, to_MaterialDocumentItem list, GoodsMovementType 101), item-level annotation semantics, and the empty-collection edge case. It omits error behavior or permissions, but these are less critical for a read-only fetch.

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 front-loaded with the main purpose, uses a compact bullet for the parameter, and every subsequent sentence adds relevant output or behavioral detail. There is no fluff or repetition.

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?

For a one-parameter read tool with an output schema, the description is complete: it states what is fetched, how to provide the input, what the SAP response looks like, what annotations are included, and the empty-result behavior. It leaves little ambiguity for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter, po_id, is explained as a 'corpus PO id' with a concrete example ('PO-8801'). This adds meaning beyond the schema, which only declares it as a required string, and fully compensates for the 0% schema description coverage.

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 and resource: 'Fetch the goods receipt(s) for a PO.' It also positions the tool as 'the GRN actual leg of the match,' distinguishing it from sibling tools like get_purchase_order and get_invoice.

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?

It provides clear context by stating the tool retrieves goods receipts for a PO within a matching workflow. It does not explicitly name alternatives or exclusions, but the intended use case is unambiguous.

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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