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AllanSJoseph

InventorySystem MCP Server

by AllanSJoseph

create_draft_bill

Creates a draft bill by adding specified products and quantities to it. Use to generate a bill draft from product IDs.

Instructions

Creates a draft bill and adds the provided products to it.

Args:
    products: A list of products, each element of this list should be a python dictionary of the format:
            - prod_id: int, a unique identifier for the product (must be a positive value)
            - quantity: int, quantity of the product (must be a positive value and cannot be zero)

Example Usage for LLM:
User: "Create a bill for 2 units of product 101 and 5 units of product 102"
LLM Input:
{
    "products": [
        {"prod_id": 101, "quantity": 2},
        {"prod_id": 102, "quantity": 5}
    ]
}

Returns:
    A confirmation message or an error message in json format if the request fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
productsYes
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It explains the creation action and return behavior (confirmation or error message in JSON). It does not mention permissions or side effects, but for a simple creation tool this is adequate.

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 well-organized, front-loading the main action, then detailing arguments, example, and return format. Every section earns its place, and the example is directly useful for LLM invocation.

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?

Given one parameter and no output schema, the description covers the essential context: purpose, parameter structure, example, and return format. It could mention edge cases like empty product lists or invalid product IDs, but that is a minor gap.

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?

Schema coverage is 0%, and the description fully compensates by specifying the exact structure of the 'products' parameter: each element is a dict with 'prod_id' and 'quantity' fields, including constraints. The example usage further clarifies the format.

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 clearly states the tool's function: 'Creates a draft bill and adds the provided products to it.' This uses a specific verb and resource, distinguishing it from sibling tools like open_bill or issue_bill.

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 usage through the example (creating a bill with products) but does not explicitly contrast with alternative flows like open_bill + add_item_to_bill. No exclusions are mentioned, so it gets a mid-range score.

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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