Provides tools to scrape product data from Walmart, including retrieving product information by URL, discovering top products by category keyword, and scraping category product listings.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@bach-walmart_scraper4 Find top products in electronics category."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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🎯 进入 MCP 广场，浏览所有可用的 MCP 服务器
🔍 搜索或找到本服务器（bach-walmart_scraper4）
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Related MCP server: Decodo MCP Server
这是一个 MCP 服务器，用于访问 Walmart Scraper4 API。
PyPI 包名: bach-walmart_scraper4
版本: 2.0.0
传输协议: stdio
pip install bach-walmart_scraper4pip install -e .# 运行（uvx 会自动安装并运行）
uvx --from bach-walmart_scraper4 bach_walmart_scraper4
# 或指定版本
uvx --from bach-walmart_scraper4@latest bach_walmart_scraper4python server.py# 安装
pip install bach-walmart_scraper4
# 运行（命令名使用下划线）
bach_walmart_scraper4此 API 需要认证。请设置环境变量:
export API_KEY="your_api_key_here"变量名 | 说明 | 必需 |
| API 密钥 | 是 |
编辑 Claude Desktop 配置文件 claude_desktop_config.json:
{
"mcpServers": {
"walmart_scraper4": {
"command": "uvx",
"args": ["--from", "bach-walmart_scraper4", "bach_walmart_scraper4"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}注意: 请将 E:\path\to\walmart_scraper4\server.py 替换为实际的服务器文件路径。
此服务器提供以下工具:
get_product_data_from_urlThis API endpoint is used to collect product information from a URL
端点: POST /api/ecommerce/walmart-scraper/products
monitor_statusSome scraping data may take longer than average. If that's the case, you'll get a task_id. Use it to retrieve your data when it's complete.
端点: GET /api/monitor-status
参数:
task_id (string) 必需: Example value: s_m195ovji14wesgd4u1
discover_top_products_in_a_category_by_keywordThis endpoint if for discovering top products from a given category keyword.
端点: POST /api/ecommerce/walmart-scraper/products-by-keyword
discover_top_products_in_a_categoryThis API endpoint scrapes top products in a given category. It takes URL as parameter.
端点: POST /api/ecommerce/walmart-scraper/category-products
传输协议: stdio
HTTP 客户端: httpx
此服务器由 API-to-MCP 工具自动生成。
版本: 2.0.0
This API endpoint scrapes top products in a given category. It takes URL as parameter.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'scrapes' but fails to explain return format, rate limits, authentication needs, or potential side effects. The claim that it 'takes URL as parameter' is not reflected in the input schema, which is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, with two short sentences, front-loaded with the purpose. However, the second sentence is inaccurate relative to the schema, which slightly detracts from its value, but structurally it is appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is far from complete. It lacks any mention of output format, pagination, error handling, or contextual guidance for choosing this tool over its sibling. The schema is empty, no output schema exists, and no annotations are provided, leaving the agent without essential operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description says 'It takes URL as parameter,' but the input schema has zero parameters and no properties. This contradicts the schema and misleads the agent into believing a URL can be supplied. Since the description adds no valid parameter meaning and actually contradicts the schema, the score is minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'scrapes top products in a given category.' It also explicitly mentions the URL parameter, which distinguishes it from the sibling 'discover_top_products_in_a_category_by_keyword' that presumably accepts a keyword instead of a URL.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a category URL is available, but it does not explicitly state when to use this tool versus alternatives like the keyword-based sibling. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
This endpoint if for discovering top products from a given category keyword.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'discovers top products' without explaining how the keyword is provided (schema has no parameters), what 'top products' means, ordering, or return structure. This is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but the typo ('if' instead of 'is') and vague phrasing ('from a given category keyword') detract from clarity. It is short but not well-structured enough for high marks.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and no output schema, the description is expected to provide more context. It lacks info on how the keyword is passed, what constitutes a top product, and how results are returned. Also, the sibling tool 'discover_top_products_in_a_category' suggests a need to explain the difference, which is absent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty, so the baseline is 4. However, the description mentions 'a given category keyword,' implying an input that is not reflected in the schema. This creates confusion about how the keyword is supplied, reducing the score from baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('discovering top products') and the basis ('a given category keyword'), distinguishing it from siblings like 'discover_top_products_in_a_category' which likely uses a category ID. However, the typo 'if' and the phrase 'from a given category keyword' adds slight ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention when to prefer this over 'discover_top_products_in_a_category' or other siblings, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
This API endpoint is used to collect product information from a URL
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only says 'collect product information' which implies a read operation, but it doesn't disclose any side effects, required auth, rate limits, error handling, or the structure of the response. This is a minimal, non-committal description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the key action ('collect product information from a URL'). It is concise and easy to read, though it lacks depth. No unnecessary words are present, and it earns its place despite being generic.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, no annotations, and a parameterless schema, so the description is the only source of guidance. It fails to specify what product information is collected, what URL formats are accepted, return value structure, or any limitations. The description is far from complete for a tool that is presumably meant to fetch and parse external data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, yet the description says 'from a URL', implying a URL input is required. There is no explanation of how the URL is passed, and no parameter exists in the schema. This is a contradiction between description and schema, making the tool's invocation unclear. The description adds misleading meaning rather than clarifying.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool collects product information from a URL, which is a clear verb+resource pairing. It distinguishes from siblings like 'discover_top_products_in_a_category' by focusing on a specific URL. However, it doesn't clarify how the URL is provided given the schema has no parameters, leaving slight ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like the discovery tools or monitor_status. It doesn't mention prerequisites, typical use cases, or exclusions. The only implicit signal is that it's URL-specific, but no explicit usage context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Some scraping data may take longer than average. If that's the case, you'll get a task_id. Use it to retrieve your data when it's complete.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | Example value: s_m195ovji14wesgd4u1 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral transparency. It states data can be retrieved when complete, but does not disclose what happens if called before completion (e.g., returns status, blocks, errors), whether the task is consumed, or any rate limits. This ambiguity is significant for a status-monitoring tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the conditional scenario, and uses no filler. Every clause contributes to understanding the tool's purpose and usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description gives essential context about async scraping but omits critical details like what the tool returns (status vs. data), behavior when the task is incomplete, and how the agent knows the data is ready. These are gaps given the absence of annotations and output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage with a simple task_id string and example. The description adds meaning by explaining that this task_id is obtained from previous scraping calls when data takes longer, which helps the agent understand the parameter's origin and purpose beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly indicates the tool is for retrieving data associated with a task_id from async scraping operations. It distinguishes itself from sibling scraping tools by focusing on retrieval after completion, though it never explicitly names the action as 'monitor status' or 'check status'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this tool when a scraping operation returns a task_id because data takes longer than average. It implicitly tells the agent to use it for polling/completing async work, though it does not explicitly mention alternatives 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v2.0.0discover_top_products_in_a_categorydiscover_top_products_in_a_category_by_keywordget_product_data_from_urlmonitor_statusThe two 'discover_top_products' tools have overlapping purposes and could be confused, with the only clear distinction being the input type (URL vs keyword) described in the text. Other tools are distinct in function.
All names use snake_case, but there is no consistent pattern: 'get_product_data_from_url' and 'discover_top_products_in_a_category_by_keyword' are long and descriptive, while 'monitor_status' is short. Verbs vary and one pair of tools differs only by a trailing qualifier.
Four tools is a reasonable scope for a scraper server. However, having two very similar discovery tools adds slight redundancy and makes the set feel less concise than it could be.
The set covers the core scraping workflow: fetching a single product, discovering top products by two methods, and checking async task status. No obvious critical gaps are present for the stated purpose.
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Scrape and crawl websites into queryable tables: products, leads, Shopify catalogs, real estate.
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