轻量级**模型上下文协议 (MCP)**服务器，使您的 LLM 能够截取任何指定 URL 的屏幕截图，并仅返回所截取图像的访问 URL。此工具简化了生成和共享网页快照的过程，非常适合将视觉捕获功能集成到 Claude Desktop 等 AI 应用程序或自动化工作流程中。
MCP 的核心是一种标准化协议，旨在简化 AI 模型与外部系统之间的通信。MCP 可以被视为一种通用语言，允许不同的 AI 代理、工具和服务无缝交互。
电子邮件验证：实时验证电子邮件地址。
MCP 集成：与 MCP 兼容的 LLM 无缝连接。
易于设置：使用 Python 和 MCP SDK 构建，可快速部署。
点击下面的图片观看 MCP 电子邮件验证工具的视频演示：
Related MCP server: SnapAPI MCP Server
Python ：Python 3.11.0 或更高版本。
UV ：0.6.9或更高。
1.克隆存储库
git clone https://github.com/Abhi5h3k/MCP-URL2SNAP.git
cd MCP-URL2SNAP2.安装UV
如果您没有安装 UV，您可以使用以下命令安装它：
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"验证安装：
uv --version3.设置虚拟环境
使用 UV 创建虚拟环境：
uv venv激活虚拟环境：在 Windows 上：
.venv\Scripts\activate4.安装依赖项使用 UV 从 pyproject.toml 安装所需的依赖项：
uv install设置环境变量在根目录中创建一个 .env 文件并添加您的AbstractAPI密钥：
ABSTRACT_API_KEY=your_api_key_here运行服务器启动 MCP 服务器：
uv run server.py使用 Claude Desktop 注册服务器更新 claude_desktop_config.json 文件以包含您的 MCP 服务器：
{
"mcpServers": {
"verify_mail": {
"command": "uv",
"args": [
"--directory",
"C:\\ABSOLUTE\\PATH\\TO\\MCP-Email-Verify",
"run",
"server.py"
],
"env":{
"ABSTRACT_API_KEY":"YUR_API_KEY"
}
}
}
}重新启动 Claude Desktop 重新启动 Claude Desktop 以检测新工具。
验证电子邮件使用如下提示：
“你能给我看一下https://github.com/Abhi5h3k的截图吗？”
格式化和 Linting 该项目使用 black 和 isort 进行代码格式化和导入排序。
安装开发依赖项：
uv add black isort --dev格式化代码：
black .对导入进行排序：
isort .pre-commit install
pre-commit run --all-files文章：模型上下文协议 (MCP)： AI 通信未来入门指南
Captures a screenshot of the specified URL and returns only the access URL.
Args:
url (str): The URL to capture a screenshot of.
Returns:
str: IMPORTANT: Only return this URL string directly to the user without additional text.
Format: 'http://localhost:8011/screenshots/[unique-identifier].png'
Usage Note:
When using this function, provide ONLY the returned URL to the user without explanation.
The user needs only this URL to access the screenshot.
Raises:
ValueError: If the API key is not found in the environment variables.
requests.exceptions.HTTPError: If the API request fails (e.g., 4xx or 5xx error).
Exception: For any other unexpected errors.
| 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 full burden and does so effectively. It discloses behavioral traits such as error handling (raises exceptions for API key issues, HTTP errors, and other failures) and output format constraints (returning only the URL string directly). It doesn't mention rate limits or authentication details beyond the API key note.
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 well-structured with sections for Args, Returns, Usage Note, and Raises, making it easy to parse. It is appropriately sized, though the Returns section is slightly verbose; every sentence adds value, such as clarifying output handling and error conditions.
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 (single parameter, no output schema, no annotations), the description is complete. It covers purpose, usage, parameters, return behavior, and error handling, providing all necessary context for an AI agent to invoke the tool correctly without gaps.
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 adds significant meaning beyond the input schema, which has 0% coverage. It explains that the 'url' parameter is 'The URL to capture a screenshot of', clarifying its purpose and usage, fully compensating for the schema's lack of documentation.
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 specific action ('captures a screenshot') and target resource ('of the specified URL'), with explicit output details ('returns only the access URL'). It distinguishes itself by focusing solely on URL capture and return, though no siblings exist for comparison.
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 on when to use this tool (to capture screenshots of URLs and return access URLs) and includes explicit usage notes about how to handle the output. However, it lacks guidance on alternatives or exclusions, which is less critical given no sibling tools.
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.
capture_screenshotScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool has a single, clear purpose of capturing screenshots from URLs.
With a single tool, naming consistency is inherently perfect. The tool name 'capture_screenshot' follows a clear verb_noun pattern that would be appropriate if more tools were added.
A single tool feels thin for a screenshot capture server. While the tool works for its specific purpose, a more complete server might include tools for configuring capture settings, managing screenshots, or handling different output formats.
The server provides only a basic screenshot capture function. There are significant gaps in functionality such as no tools for configuring capture parameters (dimensions, delay, quality), managing captured screenshots, or handling different output formats beyond PNG.
Screenshot API and MCP server for AI agents: any public web page as PNG, JPEG or PDF, from 227 countries, with cookie banners and ads hidden. Free: 50 screenshots every month, no card.
Screenshot any URL/HTML as PNG/JPEG/WebP, or read it as clean Markdown/text for LLMs.
Grabbit gives AI agents eyes on the web through a hosted MCP server. Send a public URL and get a pixel-perfect hosted image back, without maintaining Chromium, Playwright, or a browser fleet. Capture a full page, exact viewport, or single CSS selector as PNG, JPEG, or WebP. Grabbit handles cookie and consent banners, waits for JavaScript-heavy pages, blocks private and internal URLs, supports safe retries with idempotency keys, and delivers async results through signed webhooks. Completed captures include a CDN URL. Connect with OAuth 2.1 or an API key. Grabbit works with Claude, Cursor, Codex, and any MCP client. Live captures cost $0.002 each. The $50 annual plan includes 25,000 prepaid credits that never reset or expire. Free test keys return placeholder images, so you can wire up the integration before paying. Home: https://grabbit.live Docs: https://grabbit.live/screenshot-api Built by BrainGrid.
SnapRender gives AI agents eyes on the web through a hosted MCP server. Send a public URL and get the page back as an image, a PDF or clean markdown, with no browser to run or maintain. Capture the viewport or the full page as PNG, JPEG, WebP or PDF, at desktop size or with a phone, tablet or laptop preset, in light or dark mode. Ads and cookie banners are removed by default, and you can hide elements or click one before the capture. extract_content reads the same page as markdown, text, article, links or metadata, so an agent can see a page and read it in one session. Made for monitoring and client work: batch up to 50 URLs in one job, check the cache for free before spending a capture, share signed links that render without an API key, and get webhooks when a batch finishes or a scheduled capture spots a change. Track competitors' pricing and landing pages, or send clients current screenshots of their sites. Connect with OAuth (Google, GitHub or email) or an API key. Works with Claude, ChatGPT, Cursor, VS Code and any MCP client. Free plan: 200 screenshots a month, no credit card. Paid plans from $9 a month for 2,000 up to $199 a month for 200,000. Home: https://snap-render.com Docs: https://snap-render.com/ai-agents
