Converts web pages into clean Markdown format, extracting content while preserving links and structure for documentation reading, content analysis, and information gathering.
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., "@Read-Website read the latest documentation on OpenAI's API from their website"
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.
一个快速、高效的网页内容提取工具，将网页转换为干净的Markdown格式，适用于AI代理、IDE和LLM管道。 A fast and efficient web content extraction tool that converts web pages into clean Markdown format, suitable for AI agents, IDEs, and LLM pipelines.## 工具列表 Tool List
本MCP服务封装下列工具，可让模型通过标准化接口调用以下功能。 本MCP服务封装下列工具，可让模型通过标准化接口调用以下功能。
工具 Tool | 描述 Description |
read_website | Fast, token-efficient web content extraction - ideal for reading documentation, analyzing content, and gathering information from websites. Converts to clean Markdown while preserving links and structure. |
工具在线测试： https://mcp.xiaobenyang.com/inspector/1777316659753987
Online Tool test https://mcp.xiaobenyang.com/inspector/1777316659753987
Related MCP server: Puppeteer Vision MCP Server
如何获取 XBY-APIKEY ？ How to get XBY-APIKEY ?
访问小笨羊科技网站 https://xiaobenyang.com，注册用户即可获得APIKEY Visit XiaoBenYang website https://xiaobenyang.com, register and get the APIKEY.
{
"mcpServers": {
"网页内容提取工具": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "sse",
"url": "https://mcp.xiaobenyang.com/1777316659753987/sse"
}
}
}{
"mcpServers": {
"网页内容提取工具": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "streamable_http",
"url": "https://mcp.xiaobenyang.com/1777316659753987/mcp"
}
}
}{
"mcpServers": {
"网页内容提取工具": {
"command": "npx",
"args": [
"-y",
"xiaobenyang-mcp"
],
"env": {
"XBY_APIKEY": "<YOUR_XBY_APIKEY>",
"mcpId": "1777316659753987",
},
"transport": "stdio"
}
}
}
Fast, token-efficient web content extraction - ideal for reading documentation, analyzing content, and gathering information from websites. Converts to clean Markdown while preserving links and structure.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| pages | No | ||
| cookiesFile | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: 'fast, token-efficient' (performance characteristics), 'converts to clean Markdown' (output format), and 'preserving links and structure' (content handling). However, it lacks details on error handling, rate limits, authentication needs, or what happens with invalid URLs, which are important for a web scraping 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 highly concise and well-structured in two sentences. The first sentence establishes the core functionality and ideal use cases, while the second explains the output format and key features. Every word earns its place with no redundancy or unnecessary elaboration.
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 moderate complexity (web scraping with 3 parameters) and lack of annotations or output schema, the description is partially complete. It covers the purpose, performance, and output format well, but it misses crucial details like parameter semantics, error conditions, and return value structure, which are needed for effective tool invocation.
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 description coverage is 0%, and the description provides no information about the three parameters (url, pages, cookiesFile). It doesn't explain what 'pages' or 'cookiesFile' mean, their formats, or how they affect the extraction process. This leaves significant gaps in understanding parameter usage beyond what the bare schema provides.
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 ('extraction', 'reading', 'analyzing', 'gathering') and resources ('web content', 'websites', 'documentation'). It distinguishes itself by emphasizing 'fast, token-efficient' conversion to 'clean Markdown while preserving links and structure', which provides a unique value proposition even without sibling tools.
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 implied usage guidance by listing ideal scenarios ('reading documentation, analyzing content, and gathering information from websites'), but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. With no sibling tools, the lack of comparative guidance is less critical, but it still doesn't offer explicit when/when-not instructions.
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.
v1.0.0read_websiteWith only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as web content extraction, making it impossible for an agent to misselect between non-existent alternatives.
The single tool name 'read_website' follows a clear verb_noun pattern, and with no other tools present, there is no inconsistency to evaluate. The naming is straightforward and predictable for this minimal set.
One tool is too few for a server named 'Read-Website', as it suggests a narrow scope that might limit functionality. While the tool is well-described, a single tool often feels insufficient for robust web content interactions, such as handling errors, managing sessions, or providing additional utilities like summarization or filtering.
The tool surface is severely incomplete for web content extraction. It lacks essential operations such as handling different content types (e.g., PDFs, images), managing cookies or authentication, retrying failed requests, or providing metadata extraction. This gap will likely cause agent failures in real-world scenarios beyond basic reading.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Web scraping for AI agents. Converts URLs to clean, LLM-ready Markdown with anti-bot bypass.
Clean Markdown and AI-readability scoring for any URL. Built for AI agents.
Cloud scraping & crawling API for AI agents. Turn any URL into clean, LLM-ready markdown.
Converts any URL to clean, LLM-ready Markdown using real Chrome browsers
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