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FadiSheh

Lufa Farms MCP Server

by FadiSheh

lufa_remove_from_basket

Remove a specified quantity of a product from your Lufa Farms grocery basket. Provide product ID and quantity to adjust or clear cart items.

Instructions

Remove a quantity of a product from the basket.

Requires an active session.

Args: product_id: Product ID to remove. quantity: Number of units to remove (default 1). Pass a value >= the current basket quantity to remove the line entirely.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
quantityNo
product_idYes
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses that an active session is required, and explains the semantics of the quantity parameter, including the behavior of passing a value greater than or equal to the current basket quantity to remove the entire line. It does not cover error handling or side effects beyond the core behavior, but it provides key behavioral insights for a mutation tool.

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 concise and well-structured, with a single clear sentence followed by a two-item parameter list. It avoids redundant phrasing and front-loads the essential purpose. The special quantity behavior is explained in one clause without added verbosity.

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 simple two-parameter mutation tool, the description covers the core purpose, a prerequisite (active session), and parameter semantics. However, it does not describe the return value (though no output schema exists) or potential error cases such as invalid product_id. Given the tool's simplicity, the description is largely sufficient, but additional details about response or failure modes would make it more complete.

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 schema provides no descriptions for either parameter, so the description must compensate. It defines product_id as the ID to remove and quantity as the number of units (default 1), and adds the nuanced behavior of removing the entire line when quantity exceeds the current basket quantity. This goes beyond the schema's type-only definitions and gives actionable meaning.

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 with a specific verb ('Remove') and resource ('a quantity of a product from the basket'). This distinguishes it from siblings like lufa_add_to_basket and lufa_view_basket, which add and view respectively. The scope is explicit and unambiguous.

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 the tool is for removing items from a basket, but does not explicitly state when to use it over alternatives or when-not. It notes that an active session is required, which is a prerequisite but not a usage guideline comparing to other tools. No alternative tools or exclusion conditions are mentioned, leaving usage to be inferred from the name and context.

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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