ScrapingDog-MCP

walmart_search

Scrape Walmart search results by providing a Walmart search URL. Returns product titles, prices, ratings, review counts, availability, and seller information.

Instructions

Scrapes Walmart search result pages by passing any Walmart search URL, returning product titles, prices, ratings, review counts, availability, and seller info. [Credits: 5 API credits per successful request.] Notes: Pagination is handled by including page parameters in the passed Walmart search URL itself (e.g. &page=2) rather than via a separate API parameter — the docs only document the url parameter. No domain/country localization parameters documented. Returns: { search_results: [{title, totalItemCount, item: [{title, id, usItemId, type, thumbnail, canonicalUrl, rating, review_count, seller_name, availability_status, current_price, before_price, price_range_string, sponsored, shipping}]}] }

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "walmart_search"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	The full Walmart search URL to scrape. Build this URL directly from the Walmart website (e.g. https://www.walmart.com/search?q=football).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 10, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Discloses credit cost (5 API credits) and return format. With no annotations, the description carries the burden well, though it could mention rate limits or potential errors.

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?

Detailed but slightly verbose; all sentences are useful. Could be trimmed by merging some notes, but overall well-structured with key info up front.

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?

Fully covers the tool's functionality: input, behavior, return format. Even provides example return structure. No output schema needed; description is self-sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single 'url' parameter is well-explained: shows how to construct the URL and notes pagination. Schema coverage is 100%, and the description adds value beyond the schema.

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 it scrapes Walmart search results and lists returned fields (titles, prices, ratings, etc.). It distinguishes from sibling tools like walmart_product and walmart_reviews by focusing on search pages.

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?

Provides clear instructions: pass a Walmart search URL, handle pagination via URL parameters. However, it lacks explicit guidance on when to use this vs. other search tools (e.g., google_shopping) or when not to use it.

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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