Searchspring MCP. Query your entire e-commerce catalog conversationally.

This server connects your AI agent directly to a full e-commerce product catalog (Athos Commerce). You can talk to thousands of SKUs—querying stock levels, filtering by size or color, checking prices, and auditing metadata—all from the chat window. You use this for everything from customer support lookups to deep inventory checks.

General Catalog Queries

To start a broad search across the entire catalog using natural language keywords, you run the search_products tool. This function lets your agent perform a general query against all available product data, giving you an immediate list of potential matches based on what's typed. Before running a full search, you can check real-time customer behavior by calling suggest_queries.

This pulls the exact autocomplete suggestions that shoppers are currently typing into the front end, letting your agent know exactly what terms customers are using right now.

Targeted Product Discovery

If you know the product's origin, use the search_brand tool. It lists every single item available from one specific manufacturer or brand name, so you don't have to sift through irrelevant results. You can find all products within a defined hierarchy by running search_category. For example, if you want everything under 'Mens > Shoes,' this function pulls that entire group of items into the agent’s response.

When standard categories aren't enough, you execute complex search requests using specific key-value pairs via search_custom. This tool lets your agent run deep catalog queries by targeting parameters not covered by other functions.

Refining Search Results

You don't want 500 results when you only need five. To narrow down the list based on multiple criteria, use search_filtered. You format this like 'key:value,key2:value2,' letting your agent filter results by things like size and color simultaneously. If you only care about the cost, you limit product searches to a specific monetary window using search_price_range.

This tool requires you to specify both a minimum and maximum dollar amount for the desired items. To make sure you see the most relevant inventory, you can reorder any set of results based on criteria like price or date by calling search_sorted. You just tell it what key to sort by and which direction ('asc' or 'desc') you need.

If the initial list is massive—like 10,000 SKUs—you fetch a specific chunk of results using search_pagination, keeping your agent’s response manageable.

Deep Item Verification

If you know the exact Stock Keeping Unit number, use search_sku. This tool is critical because it pulls all specifications, current pricing details, and live stock status for one single product. It gives you total verification that the product metadata is synced up and accurate before a customer asks about it.

Basically, your agent handles everything from keyword searches with search_products to confirming the exact inventory count using search_sku. You're running deep e-commerce logic right inside the chat. You don't have to leave the window or manually browse a storefront; you just tell your AI client what product data you need, and it gets it.