Provides tools to search for Sam's Club products by keyword and category, locate nearby warehouse locations via zip code or city, and retrieve detailed membership information including tiers, annual fees, and benefits.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Connector for Sam's Club find organic olive oil and show me prices"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) server that exposes Sam's Club product search, store locator, and membership information as tools for AI assistants.
search_productsSearch Sam's Club products by keyword, with optional category filtering and result count control.
Parameters:
Parameter | Type | Required | Description |
| string | Yes | Search term (e.g. "olive oil", "TV", "laundry detergent") |
| string | No | Filter by category: |
| number | No | Max results to return (default: 10, max: 15) |
Example response fields: id, name, category, price, unit, availability, rating, reviews
find_clubsFind Sam's Club warehouse locations near a zip code or city.
Parameters:
Parameter | Type | Required | Description |
| string | Yes | Zip code (e.g. |
| number | No | Search radius in miles (default: 25) |
Example response fields: club id, name, address, phone, hours, services, distanceMiles
get_membership_infoReturns Sam's Club membership tiers, annual fees, and benefits. Takes no parameters.
Response includes: membership types (standard and Plus), annual fees, benefits lists, add-on pricing, free trial details, and contact information.
Related MCP server: Fake Store API MCP Server
npm install
npm run buildnode dist/index.jsAdd to your claude_desktop_config.json:
{
"mcpServers": {
"samsclub": {
"command": "node",
"args": ["/path/to/mcp-samsclub/dist/index.js"]
}
}
}Or if installed globally via npm:
{
"mcpServers": {
"samsclub": {
"command": "mcp-samsclub"
}
}
}# Install dependencies
npm install
# Build
npm run build
# Pack for distribution
npm packThis connector uses realistic mock data and does not make live requests to Sam's Club servers. It is intended for development, testing, and demonstration purposes.
MIT
Find Sam's Club warehouse locations near a given zip code or city. Returns store details including address, hours, phone, and available services.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | Zip code (e.g. '72712') or city name (e.g. 'Bentonville, AR') | |
| radius | No | Search radius in miles (default: 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns store details, but does not disclose important behavioral traits such as rate limits, authentication needs, error handling, or whether the search is real-time or cached. The description is functional but lacks depth for safe and effective use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, consisting of two concise sentences that efficiently convey the tool's purpose and output. Every sentence earns its place by providing essential information without redundancy or unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It covers the basic purpose and output, but lacks behavioral context and detailed usage guidelines. Without an output schema, it partially describes return values, but more completeness is needed for optimal agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents both parameters ('location' and 'radius') with clear descriptions. The description adds minimal value beyond the schema by mentioning 'zip code or city' and 'search radius in miles', but does not provide additional syntax, format details, or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Find') and resource ('Sam's Club warehouse locations'), and distinguishes it from siblings by focusing on location search rather than membership or product information. It explicitly mentions the search criteria ('near a given zip code or city') and the type of data returned ('store details including address, hours, phone, and available services').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying the search context ('near a given zip code or city'), but does not explicitly state when to use this tool versus alternatives like 'get_membership_info' or 'search_products'. It provides some guidance on input parameters but lacks explicit exclusions or comparisons to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current Sam's Club membership types, annual fees, benefits, and pricing information. No parameters required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states 'No parameters required,' which is useful context beyond the schema. However, it doesn't describe other behavioral traits like response format, error conditions, or data freshness, leaving gaps for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose and includes essential usage note. Every word earns its place with no redundancy or waste, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no annotations, no output schema), the description is minimally complete. It covers the purpose and parameter requirement but lacks details on output format or behavioral nuances, which could be helpful since there's no output schema to rely on.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of parameters. The description adds value by explicitly stating 'No parameters required,' which reinforces this and provides semantic clarity. This compensates adequately, though not extensively needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get') and the exact resources ('current Sam's Club membership types, annual fees, benefits, and pricing information'). It distinguishes this tool from sibling tools like 'find_clubs' (location-based) and 'search_products' (product-focused) by specifying membership information retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Get current Sam's Club membership...information'), but it doesn't explicitly state when not to use it or name alternatives. It implies usage for membership-related queries rather than club locations or products, though not with explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Sam's Club products by a text query. Returns matching products with pricing, availability, and ratings.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g. 'olive oil', 'TV', 'paper towels') | |
| category | No | Optional product category to filter results. Valid values: Electronics, Grocery, Household, Meat & Seafood, Appliances, Outdoor | |
| maxResults | No | Maximum number of results to return (default: 10, max: 15) |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0find_clubsget_membership_infosearch_productsScored across 3 tools
Each tool has a clearly distinct purpose: find_clubs targets location search, get_membership_info provides membership details, and search_products handles product discovery. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern (find_clubs, get_membership_info, search_products) with clear, descriptive verbs. The naming is uniform and predictable across the set.
With 3 tools, the count is reasonable for a retail-focused server, covering key areas like locations, membership, and products. It is slightly lean but well-scoped, as each tool serves a distinct, essential function without redundancy.
The tools cover core informational aspects (find, get, search) but lack CRUD operations for a full retail experience, such as managing a cart, placing orders, or updating membership. While the surface is functional for queries, there are notable gaps in transactional capabilities.
Sandbox workspace tools: search, file read, DB queries, integrations. Returns synthetic data.
AI-callable tools for API mocking, testing, monitoring, security, and automation.
Hosted MCP endpoint with realistic fake data for prototyping agents. 12 tools, no setup.
Pay-per-use tool marketplace for AI agents. Search, price-check, and call APIs via MCP.