Skip to main content
Glama
Crawlora-org

Crawlora MCP

Official

walmart_product_reviews

Extract Walmart product review data including average rating, total count, per-star breakdown, recommended percentage, top positive and negative reviews, and sample recent reviews from a single product page.

Instructions

Get Walmart product reviews. Returns the reviews snapshot embedded in a Walmart product page: average rating, total review count, the per-star rating breakdown, the recommended percentage, the top positive and top negative review, and a sample of recent reviews. This is a single on-page snapshot, not a full paginated feed. A product that exists but has no reviews returns zero counts and an empty reviews list. Credential-free public Walmart data, rendered from the product page through proxied browser renderers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
item_idYesWalmart item id (the numeric id in a /ip/{id} URL)
Behavior5/5

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

No annotations provided, so full burden on description. It explains data source (proxied browser renderers), credential-free nature, and edge cases (empty reviews return zero counts).

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

Conciseness5/5

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

Four sentences, front-loaded with main action, no redundant information, every sentence adds value.

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?

No output schema, but description fully explains output contents (average rating, breakdowns, sample reviews) and edge cases. Covers all needed context for this simple tool.

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 covers 100% of parameter with clear description. Description adds no extra meaning beyond schema, so baseline 3 is appropriate.

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 the verb 'Get' and specific resource 'Walmart product reviews', listing exact data returned (average rating, breakdown, etc.) and distinguishing from a full paginated feed.

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?

Explicitly states this is a single on-page snapshot, not a full feed, and describes behavior for no reviews. However, it lacks explicit comparison to sibling tools like walmart_product that might also contain review data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Crawlora-org/crawlora-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server