Enables programmatic scraping of product information, prices, and inventory data from Amazon.
Allows for the extraction of product listings, pricing, and market data from eBay.
Provides AI-optimized web search results and specialized tools for extracting location and business data from Google Maps.
Facilitates social media data extraction and profile monitoring on Instagram.
Provides a tool to convert any web page content into clean Markdown, optimized for content extraction and AI documentation.
Enables gathering of structured product details and inventory availability from Walmart.
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., "@The Web MCP What's the latest news on AI regulations in the EU right now?"
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.
The Web MCP 是为 AI 助手赋予真正「上网能力」的通道。告别过时回答，也不再出现“我无法访问实时信息”的窘境——只需稳定、顺畅、可用的网络访问。
由全球领先的Bright Data Web 数据平台打造，这个 MCP 服务器确保你的 AI 不再被封锁、限流或被验证码拦住。
Related MCP server: Spider MCP Server
🔍 实时调研——获取最新价格、新闻与实时数据
🛍️ 电商情报——监测产品、价格与库存
📊 市场分析——跟踪竞品与行业趋势
🤖 AI 代理——构建真正能「浏览网页」的智能体
📝 内容创作——为写作获取最新可信的信息
🎓 学术研究——高效汇聚多源公开数据
零门槛使用，只需在你的 MCP 客户端中添加以下 URL：
https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN_HERE在 Claude Desktop 中设置：
打开：Settings → Connectors → Add custom connector
名称：Bright Data Web
URL：https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN
点击 “Add”，完成！✨
{
"mcpServers": {
"Bright Data": {
"command": "npx",
"args": ["@brightdata/mcp"],
"env": {
"API_TOKEN": "<your-api-token-here>"
}
}
}
}💡 注意：Pro 模式不包含在免费套餐内，按使用量额外计费。
✅ “特斯拉当前股价是多少？”
✅ “现在东京评分最高的餐厅有哪些？”
✅ “纽约今天的天气预报是什么？”
✅ “本周有哪些电影上映？”
✅ “今天 Twitter 上的热点话题是什么？”注：以下视频展示的是早期版本，新的演示即将上线！🎥
https://github.com/user-attachments/assets/59f6ebba-801a-49ab-8278-1b2120912e33
https://github.com/user-attachments/assets/61ab0bee-fdfa-4d50-b0de-5fab96b4b91d
工具 | 描述 | 适用场景 |
🔍 | 面向 AI 优化的 Web 搜索 | 研究、查证、时事 |
📄 | 将任意网页转为干净的 Markdown | 内容抽取、文档化 |
类别 | 工具 | 描述 |
浏览器控制 |
| 全功能浏览器自动化 |
Web 数据 API |
| 结构化数据抽取 |
电商 | 产品抓取器 | Amazon、eBay、Walmart 等 |
社媒 | 社交抓取器 | Twitter、LinkedIn、Instagram |
地图与本地 | 位置工具 | Google 地图、商家数据 |
无需任何配置，直接体验 Web MCP：
{
"mcpServers": {
"Bright Data": {
"command": "npx",
"args": ["@brightdata/mcp"],
"env": {
"API_TOKEN": "your-token-here"
}
}
}
}{
"mcpServers": {
"Bright Data": {
"command": "npx",
"args": ["@brightdata/mcp"],
"env": {
"API_TOKEN": "your-token-here",
"PRO_MODE": "true", // 启用 60+ 全部工具
"RATE_LIMIT": "100/1h", // 自定义限速
"WEB_UNLOCKER_ZONE": "custom", // 自定义解封 Zone
"BROWSER_ZONE": "custom_browser" // 自定义浏览器 Zone
}
}
}
}解决：安装 Node.js，或使用 node 的完整路径：
"command": "/usr/local/bin/node" // macOS/Linux
"command": "C:\\Program Files\\nodejs\\node.exe" // Windows解决：在客户端设置中将超时提高到 180s
解决：确认 API Token 有效且具备相应权限
解决：检查网络与防火墙设置
我们欢迎各种形式的贡献：
请遵循 Bright Data 的代码规范。
MIT © Bright Data Ltd.
Scrape a single webpage URL with advanced options for content extraction and get back the results in MarkDown language. This tool can unlock any webpage even if it uses bot detection or CAPTCHA.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
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 adds valuable context about capabilities ('unlock any webpage even if it uses bot detection or CAPTCHA') and output format ('MarkDown language'), but doesn't cover important behavioral aspects like error handling, rate limits, authentication requirements, or what 'advanced options' specifically entail.
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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality and output format, while the second adds important capability context. There's zero wasted language and it's front-loaded with the main purpose.
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?
Given the tool's complexity (web scraping with advanced capabilities), no annotations, no output schema, and only 0% schema description coverage, the description is adequate but has clear gaps. It covers the basic purpose and some capabilities well, but doesn't address error cases, performance characteristics, or detailed behavioral expectations that would be important for an AI agent.
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?
With 0% schema description coverage and only 1 parameter, the description compensates well by explaining the parameter's purpose ('scrape a single webpage URL') and context. While it doesn't provide format details beyond what the schema indicates (URI format), it adds meaningful semantic context about what the URL parameter represents in this scraping context.
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 tool's purpose with specific verbs ('scrape a single webpage URL') and resources ('webpage'), and distinguishes it from siblings by specifying 'single webpage' (vs. batch operations) and 'MarkDown language' output format. It provides a complete picture of what the tool does.
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 for when to use this tool ('scrape a single webpage URL'), but doesn't explicitly mention when not to use it or name alternatives. It implies usage for single-page scraping with advanced extraction needs, but lacks explicit comparison to sibling tools like scrape_batch for multiple URLs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scrape multiple webpages URLs with advanced options for content extraction and get back the results in MarkDown language. This tool can unlock any webpage even if it uses bot detection or CAPTCHA.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | Array of URLs to scrape (max 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: batch processing of multiple URLs (implied by 'multiple webpages'), advanced content extraction, output in MarkDown, and ability to bypass bot detection/CAPTCHA. However, it lacks details on rate limits, error handling, authentication needs, or what 'advanced options' entail, leaving gaps for a mutation-like operation.
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 concise with two sentences that efficiently convey core functionality. The first sentence covers purpose and output, while the second adds behavioral context. No wasted words, though it could be more structured by explicitly separating features from limitations.
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?
Given no annotations, no output schema, and a single parameter with full schema coverage, the description is moderately complete. It covers what the tool does and key capabilities (bypassing bot detection), but lacks details on return values, error cases, or performance constraints, which are important for a web scraping tool with potential complexity.
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?
Schema description coverage is 100%, with the schema fully documenting the 'urls' parameter (array of URIs, min 1, max 10). The description adds no parameter-specific semantics beyond implying batch processing through 'multiple webpages URLs.' Since schema coverage is high, baseline 3 is appropriate as the description doesn't significantly enhance parameter understanding.
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 tool's purpose: 'Scrape multiple webpages URLs with advanced options for content extraction and get back the results in MarkDown language.' It specifies the verb (scrape), resource (webpages), and output format (MarkDown). However, it doesn't explicitly differentiate from sibling tools like scrape_as_markdown, which might be for single URLs, so it misses full sibling distinction.
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 mentions 'advanced options for content extraction' and ability to 'unlock any webpage even if it uses bot detection or CAPTCHA,' but doesn't specify when to choose this over scrape_as_markdown or search_engine tools. No explicit alternatives or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scrape search results from Google, Bing or Yandex. Returns SERP results in markdown (URL, title, description)
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| engine | No | ||
| cursor | No | Pagination cursor for next page |
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 scraping and the return format, but lacks critical details such as rate limits, authentication needs, potential for blocking, or whether it's a read-only operation. For a scraping tool with zero annotation coverage, this is a significant gap in transparency.
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 highly concise and front-loaded: two sentences efficiently cover the action, supported engines, and output format without wasted words. Every sentence earns its place by providing essential information in a compact form.
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?
Given the complexity of a scraping tool with no annotations, no output schema, and low schema coverage, the description is incomplete. It omits behavioral risks (e.g., rate limiting), error handling, and detailed output expectations beyond 'markdown'. For a tool that interacts with external APIs and returns unstructured data, more context is needed for safe and effective use.
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?
Schema description coverage is low (33%), with only the 'cursor' parameter documented. The description adds minimal value beyond the schema: it implies the 'query' parameter is for search terms and 'engine' selects from the listed options, but doesn't explain semantics like query formatting or cursor usage. Baseline 3 is appropriate as the description partially compensates but doesn't fully address the coverage gap.
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 tool's purpose: 'Scrape search results from Google, Bing or Yandex' specifies the verb (scrape) and resource (search results), and 'Returns SERP results in markdown' indicates the output format. However, it doesn't explicitly differentiate from sibling tools like scrape_as_markdown or search_engine_batch, which appear related but have unspecified distinctions.
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 its siblings (scrape_as_markdown, scrape_batch, search_engine_batch). It mentions the supported search engines but offers no context on alternative scenarios, prerequisites, or exclusions, leaving the agent to infer usage based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run multiple search queries simultaneously. Returns JSON for Google, Markdown for Bing/Yandex.
| Name | Required | Description | Default |
|---|---|---|---|
| queries | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses output format differences (JSON for Google, Markdown for Bing/Yandex), which is useful behavioral context. However, it doesn't mention rate limits, authentication needs, error handling, or what 'simultaneously' entails (parallel vs sequential). For a batch operation tool, this leaves significant gaps in understanding its behavior.
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?
Extremely concise with two sentences that each add value. First sentence states core functionality, second sentence provides critical output format information. No wasted words, perfectly front-loaded with essential information.
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?
Given no annotations, 0% schema description coverage, and no output schema, the description is incomplete for a batch search tool. It covers purpose and output formats but misses parameter explanations, error conditions, rate limits, and detailed behavioral traits. For a tool handling multiple search engines with different output formats, more context is needed.
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?
Schema description coverage is 0%, so the description must compensate. It adds some meaning by mentioning engine-specific output formats, which relates to the 'engine' parameter. However, it doesn't explain the 'queries' array structure, 'cursor' parameter purpose, or the 1-10 item limit. The description provides partial context but doesn't fully compensate for the schema's lack of descriptions.
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 tool's purpose: 'Run multiple search queries simultaneously' specifies the verb and resource. It distinguishes from siblings like 'search_engine' (likely single query) and 'scrape_as_markdown'/'scrape_batch' (different operations), though not explicitly named. However, it doesn't fully differentiate from 'scrape_batch' which might also handle multiple items.
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?
No explicit guidance on when to use this tool versus alternatives is provided. The description mentions output formats for different engines, but doesn't state when to choose this batch tool over single-query tools like 'search_engine' or scraping tools. Usage context is implied but not clearly defined.
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.
scrape_as_markdownscrape_batchsearch_enginesearch_engine_batchEach tool has a clearly distinct purpose: scrape_as_markdown handles single webpage scraping, scrape_batch handles multiple webpages, search_engine scrapes single search queries, and search_engine_batch handles multiple search queries. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent snake_case pattern with clear verb_noun structures: scrape_as_markdown, scrape_batch, search_engine, and search_engine_batch. The naming is predictable and aligns well with the actions each tool performs.
With 4 tools, the server is well-scoped for web scraping and search functionality. Each tool earns its place by covering distinct aspects of the domain: single and batch operations for both webpage scraping and search engine queries, avoiding bloat while providing essential coverage.
The tool set covers core web scraping and search operations effectively, including single and batch modes. A minor gap exists in lacking tools for more advanced interactions like form submission or dynamic content handling, but the provided tools support key workflows without dead ends.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Direct access to 40+ scraping and search tools. Extract structured data from Google (Search, Maps, Trends), Amazon, Airbnb, Social Media, and any web page directly into your AI agent.
Live web access for agents: scrape, SERP search, crawl/map, 74 collectors, datasets, proxies.
Web data tools for AI agents: pages as markdown, search, maps, commerce, jobs, AI answers.
Enable language models to perform advanced AI-powered web scraping with enterprise-grade reliabili…
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