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markswendsen-code

MCP Connector for Sam's Club

search_products

Search Sam's Club products by text query to find items with pricing, availability, and ratings. Filter results by category and limit the number of matches.

Instructions

Search Sam's Club products by a text query. Returns matching products with pricing, availability, and ratings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (e.g. 'olive oil', 'TV', 'paper towels')
categoryNoOptional product category to filter results. Valid values: Electronics, Grocery, Household, Meat & Seafood, Appliances, Outdoor
maxResultsNoMaximum number of results to return (default: 10, max: 15)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions what the tool returns (products with pricing, availability, ratings) but doesn't describe important behavioral aspects like rate limits, authentication requirements, error conditions, pagination, or whether this is a read-only operation. The description provides basic output information but misses key operational details.

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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, and the second sentence describes the return value. There's zero wasted text, and the information is front-loaded with the most important details first.

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?

For a search tool with 3 parameters and no output schema, the description provides adequate basic information but has significant gaps. It explains what the tool does and what it returns, but without annotations or output schema, it doesn't cover important contextual details like response format, error handling, or operational constraints. The description is minimally viable but incomplete for optimal agent understanding.

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 100%, so the schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions searching 'by a text query' which aligns with the 'query' parameter but provides no additional syntax, format, or usage details. Baseline 3 is appropriate when the schema does all the parameter documentation work.

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 specific action ('Search'), resource ('Sam's Club products'), and scope ('by a text query'). It distinguishes this tool from its siblings (find_clubs, get_membership_info) by focusing on product search rather than club locations or membership information.

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 context (searching for products) but doesn't explicitly state when to use this tool versus alternatives. No guidance is provided about prerequisites, limitations, or comparisons with other search methods. The context is clear but lacks explicit when/when-not instructions.

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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