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damurka

jumia-vendor-center

by damurka

create_consignment

Create an inbound stock shipment to a Jumia warehouse by specifying SKUs, quantities, label codes, and shipping date. Generates a purchase order number for tracking.

Instructions

Create a consignment (inbound stock shipment to a Jumia warehouse). products: [{"sku": "...", "quantity": 10, "labelCode": "..."}]. shipping_date format is 'YYYY-MM-DD HH:mm:ss' (not ISO-8601, despite looking like it). Returns {purchaseOrderNumber}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentNo
shop_idYes
productsYes
shipping_dateYes
business_client_codeYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

There are no annotations, so the description carries the behavioral disclosure burden. It adds meaningful details beyond the raw schema: a warning that shipping_date is not ISO-8601 despite appearances, and a note that the call returns {purchaseOrderNumber}. It does not mention failure modes or side effects, but the core behavior is clearly disclosed.

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 compact and front-loaded. Every sentence adds value: the definition, the products shape, the date-format warning, and the return value. There is no filler or repetition of schema information.

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

Completeness3/5

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

The description covers the tricky parameters and the return value, but with no annotations and no output schema, the agent is still left to infer the meaning of required parameters like business_client_code. It is adequate for a straightforward call if the agent can infer the rest from parameter names, but not fully complete.

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 description coverage is 0%, so the description must compensate. It clarifies the structure of the `products` array with sku, quantity, and labelCode, and explains the shipping_date format. However, it leaves shop_id, business_client_code, and comment semantically unexplained, relying on their names.

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 ('Create') and a clear resource ('consignment') with an explanatory parenthetical ('inbound stock shipment to a Jumia warehouse'). This distinguishes it from sibling tools like update_consignment and clearly communicates the domain.

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?

The description implies when to use the tool by defining a consignment as an inbound stock shipment to a warehouse. However, it does not explicitly mention alternatives or when not to use it, such as using update_consignment for modifying an existing consignment.

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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