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walmart_search: GET /

hasdata_walmart_search_getSearchResults
Read-only

Get Walmart Search Results

Runs a keyword or category search on a chosen Walmart storefront with pagination, price range and fulfillment filters, facet narrowing, and sort order (best match, price low-to-high, price high-to-low, bestseller, highly rated, new arrivals). Returns the product list with item id, title, product URL, brand, price and strikethrough before-price, star rating, review count, seller, availability, sponsored flag, badges, SNAP EBT eligibility, Walmart+ savings, variants, other offers and fulfillment details (shipping, pickup, delivery messages), plus the facets block listing every filter the query supports with the exact value to send back in the facet parameter, pagination, and the store the prices belong to. Use for SERP monitoring, price and assortment tracking, share-of-shelf analysis, competitor discovery, and harvesting item ids to feed downstream product or reviews endpoints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoThe search term for which to get the search results. It can be omitted only when `catId` is provided, to browse a whole category instead of searching.
urlNoA full Walmart search or category URL to scrape as is. When provided, it overrides `q`, `catId` and the other search parameters, and the storefront is taken from the URL itself.
pageNoPage number for pagination (e.g., `1` for the first page, `2` for the second page, etc.). Walmart stops serving results after roughly page 10, returning an empty page beyond that.
sortNoThe sorting option for the search results.
catIdNoWalmart category id, taken from a category URL (for example `976759_1086446_1229651`). Combine it with `q` to search inside a category, or send it alone to browse the whole category. Required unless `q` is provided.
facetNoWalmart filter in the `name:value` form, for example `brand:Great Value`. Every value available for a query is listed in the facets block of the response, each one carrying the exact string to send back here, so a first unfiltered request tells you what can be filtered on. To combine filters, put each one on its own line. The double-pipe form Walmart's own urls use `brand:Great Value||retailer_type:Walmart` is also accepted.
domainNoWalmart storefront to search. Each storefront has its own catalog, prices and currency. Default is `walmart.com`.
languageNoLanguage of the results. Availability depends on the storefront - `walmart.com` serves `en` and `es`, `walmart.ca` serves `en` and `fr`. A language the storefront does not support falls back to its default.
maxPriceNoUpper bound of the price range, in the storefront currency.
minPriceNoLower bound of the price range, in the storefront currency.
deliveryTypeNoKeep only the products available with the selected fulfillment method. Default is `shipping`. Note that `pickup` is answered against one Walmart store, so an item in stock nationally can still be unavailable for `pickup` nearby - the response then reports that nothing matched the filters rather than that the query had no results.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/non-destructive, so the safety profile is covered. The description adds real behavioral value by describing the response shape (product fields, facets block, pagination, store the prices belong to) and the facet round-trip behavior. It omits rate limits or auth details, but the added context is solid for a read-only scrape.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded in the first sentence, which is good, but the second sentence is an extremely long run-on enumerating every returned field. With no output schema this enumeration has some value, but the wall-of-fields style hurts scanability.

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?

No output schema exists, so the description carrying the return-value inventory is appropriate and it does cover the response well. Combined with the fully documented schema, an agent has enough to call the tool correctly, with only minor gaps around rate limits or failure behavior.

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 documents q, url, page, sort, catId, facet, domain, language, prices, and deliveryType thoroughly. The description largely restates schema content (sort order names, price/fulfillment filters) without adding new format or precedence details beyond what the schema provides, so baseline 3 applies.

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 first sentence states a specific verb and resource ('Runs a keyword or category search on a chosen Walmart storefront') and scopes the capabilities (pagination, price range, fulfillment filters, facets, sort). It implicitly distinguishes itself from the product and reviews siblings by noting it harvests item ids 'to feed downstream product or reviews endpoints.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives concrete usage contexts (SERP monitoring, price/assortment tracking, share-of-shelf, competitor discovery) and routes the agent to product/reviews endpoints for downstream work. It does not state explicit when-not conditions, but the context is clear enough to select the tool.

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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