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HasData

Walmart MCP Server

walmart_search: GET /

hasdata_walmart_search_getSearchResults

Search Walmart by keyword or category, applying price, fulfillment, and facet filters with sorting. Retrieve detailed product results for SERP monitoring, price tracking, and competitor analysis.

Instructions

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. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden: it discloses that this is a read-only search operation and describes the full response shape, including product items, facets block, pagination, and store attribution. It doesn't mention rate limits or edge cases like the page-10 cutoff, but those are covered in the parameter descriptions and the search framing gives an accurate side-effect-free mental model.

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

Conciseness4/5

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

The description is a single dense paragraph but is front-loaded with the core action and packs all key capability and output information into one long sentence. It is appropriately sized for an 11-parameter tool with no output schema, though it could be broken into shorter sentences or bullets for easier scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with no output schema, the description thoroughly covers return value composition (items, facets, pagination, store) and practical use cases, while the schema covers all 11 parameters at 100% coverage. The combination is sufficient for an agent to select this tool over the product/reviews siblings and invoke it correctly.

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 baseline is 3; the description broadly mentions filters, facets, and sort options but adds little beyond the schema's parameter documentation. The only mild value-add is connecting the facets block to the facet parameter's exact-value contract, though even that is detailed in the schema's facet description.

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 opens with a specific verb+resource statement, 'Get Walmart Search Results,' and expands on the exact capability: keyword/category search with filters, facets, and sort order on a chosen storefront. It lists the returned data and ends by framing the tool as upstream ID harvesting for product and reviews endpoints, which distinguishes it from the product and reviews siblings.

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?

The description gives explicit use cases: SERP monitoring, price/assortment tracking, share-of-shelf analysis, competitor discovery, and harvesting item ids for downstream product or reviews endpoints. It does not explicitly name the sibling tools or state when not to use it, but the downstream-product/reviews phrasing clearly implies the product and reviews tools are the alternatives.

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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