ScreenshotOne MCP Server
OfficialScreenshotOne MCP 服务器
ScreenshotOne的MCP(模型上下文协议)服务器的官方实现。
工具
render-website-screenshot:渲染网站的屏幕截图并将其作为图像返回。
Related MCP server: PuppeteerMCP Server
用法
构建它
始终先安装依赖项并构建它:
npm run install && npm run build获取您的 ScreenshotOne API 密钥
在ScreenshotOne注册并获取您的 API 密钥。
使用 Claude 桌面版
将以下内容添加到您的~/Library/Application\ Support/Claude/claude_desktop_config.json :
{
"mcpServers": {
"screenshotone": {
"command": "node",
"args": ["path/to/screenshotone/mcp/build/index.js"],
"env": {
"SCREENSHOTONE_API_KEY": "<your api key>"
}
}
}
}独立或用于其他项目
SCREENSHOTONE_API_KEY=your_api_key && node build/index.js执照
ScreenshotOne MCP Server根据 MIT 许可证获得许可。
Available Tools
1 toolrender-website-screenshotB
Renders a screenshot of a website and returns it as an image or a JSON with the cache URL (preferred for full-page screenshots).
| Name | Required | Description | Default |
|---|---|---|---|
| block_ads | No | Block ads | |
| block_banners | No | Block cookie, GDPR, and other banners and popups | |
| cache | No | Cache the screenshot to get the cache URL | |
| cache_key | No | Cache key to generate a new cache URL for each screenshot, e.g. timestamp | |
| full_page | No | Render the full page screenshot of the website | |
| image_quality | No | Image quality | |
| response_type | No | Response type: JSON (when the cache URL is needed) or the image itself | by_format |
| url | Yes | URL of the website to screenshot |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'returns' outputs but doesn't describe what happens during rendering (e.g., page loading, timeout behavior, error handling), whether it's idempotent, or any rate limits. The description adds minimal context beyond the basic operation, leaving significant behavioral gaps for a tool with 8 parameters.
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, efficient sentence that front-loads the core purpose and key output options. It avoids redundancy and wastes no words, though it could be slightly more structured by separating purpose from usage guidance. Every part earns its place, making it highly concise.
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 (8 parameters, no output schema, no annotations), the description is minimally complete. It covers the basic operation and output types but lacks details on error conditions, performance characteristics, or example usage. Without annotations or output schema, more behavioral context would be helpful, but it's adequate for a starting point.
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%, so the schema already documents all parameters thoroughly. The description adds marginal value by hinting at the relationship between 'full_page' and cache URL preference, but doesn't provide additional syntax, format details, or usage examples beyond what the schema specifies. This meets the baseline for high schema coverage.
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: 'Renders a screenshot of a website' with the specific verb 'renders' and resource 'website screenshot'. It distinguishes the output options (image or JSON with cache URL) but doesn't need sibling differentiation since no sibling tools exist. The purpose is specific and unambiguous.
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 mentioning 'preferred for full-page screenshots' for the cache URL option, suggesting when to choose JSON over image output. However, it lacks explicit when/when-not instructions or alternatives, and doesn't address prerequisites like network access or authentication needs. With no sibling tools, this is adequate but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
render-website-screenshot
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has consistent naming, as there are no other tools to compare against. The name 'render-website-screenshot' follows a clear verb-noun pattern.
A single tool is too few for most server purposes, as it limits functionality and flexibility. While it might suffice for a minimal screenshot service, it feels thin and under-scoped for typical agent workflows.
The tool covers the core action of rendering website screenshots, but there are notable gaps such as configuration options (e.g., viewport size, quality settings) or management functions (e.g., listing cached screenshots). This could lead to agent workarounds for advanced use cases.
Maintenance
Related MCP Connectors
An MCP server that integrates with Discord to provide AI-powered features.
MCP server for OpenAI API (chat completions, image generation, embeddings) via AceDataCloud
MCP server that lets AI assistants use all OneSchema features exposed via the public API.
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.
Related MCP Servers
- AlicenseBqualityDmaintenanceAn MCP server providing web development tools such as screen capturing capabilities that let AI agents take and work with screenshots of the user's screen.239 npm15MIT
- AlicenseBqualityDmaintenanceAn MCP server that enables AI assistants to capture and analyze web page screenshots using Puppeteer, supporting multi-breakpoint captures, error reporting, and page interactions.128 npm6MIT
- AlicenseBqualityBmaintenanceAn MCP server that enables capturing website screenshots with full-page, element-specific, and device-responsive capabilities through natural language commands.316 npm62MIT
- AlicenseAqualityDmaintenanceMCP server for ScreenshotFreeAPI – capture website screenshots, render HTML/PDF, and fetch app store listing screenshots from any MCP-compatible AI client.710 npmMIT