MCP Webcam Server
mcp-网络摄像头
使用您的网络摄像头将实时图像发送到 Claude Desktop(或其他 MCP 客户端)。
提供"capture"和"screenshot"工具,允许 Claude 从网络摄像头拍摄一帧或启动截图。
还提供current view from the webcam 。
安装
NPM 包是@llmindset/mcp-webcam 。
为您的平台安装最新版本的NodeJS ,然后将以下内容添加到claude_desktop_config.json文件的mcpServers部分:
"webcam": {
"command": "npx",
"args": [
"-y",
"@llmindset/mcp-webcam"
]
}只要您使用 Claude Desktop 0.78 或更高版本,它就可以在 Windows 和 MacOS 上运行。
采用单个参数来设置嵌入式 Express 服务器的端口。
默认端口为3333 (避免与 Inspector 一起使用时发生冲突)。
Related MCP server: Webcam MCP
用法
启动 Claude Desktop,并连接到http://localhost:3333 。然后,您可以让 Claude get the latest picture from my webcam ,或者Claude, take a look at what I'm holding ,或者what colour top am i wearing?您可以“冻结”当前图像,该图像将返回给 Claude,而不是实时捕获。
您可以请求屏幕截图 - 导航到浏览器,以便在请求到达时引导截取区域。屏幕截图会自动调整大小,以便 Claude 轻松管理(如果您拥有 4K 屏幕,这将非常有用)。该按钮用于测试特定平台的屏幕截图用户体验 - 它除了帮助您准备 Claude 发起的请求外,不执行任何其他操作。注意:此功能在 Safari 上无效,因为它需要人工启动。
MCP 采样
按下“我拿着什么?”按钮向客户端发送采样请求,其中包含图像和问题What is the User holding? 。
提示:Claude Desktop 目前不支持采样功能。如果您需要能够处理多模态采样请求的客户端,请尝试https://github.com/evalstate/fast-agent/ 。
其他说明
确实如此。
建立此 MCP 服务器是为了演示如何在 MCP 服务器上公开用户界面,以及如何将实时资源返回给 Claude Desktop。
如果您想构建本地交互式 MCP 服务器,这个项目可能会有用。
感谢https://github.com/tadasant提供的测试和设置帮助。
请阅读https://llmindset.co.uk/posts/2025/01/resouce-handling-mcp上的文章,了解有关在 LLM / MCP 聊天应用程序中处理文件和资源的更多详细信息,以及为什么您可能想要这样做。
第三方 MCP 服务
Available Tools
2 toolscaptureARead-only
Gets the latest picture from the webcam. You can use this if the human asks questions about their immediate environment, if you want to see the human or to examine an object they may be referring to or showing you.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true and openWorldHint=true, indicating safe, non-destructive operation with potential for varied outcomes. The description adds valuable context by specifying it captures from 'the webcam' and returns 'the latest picture,' clarifying the source and immediacy of the data, which goes beyond what annotations alone convey.
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 front-loaded with the core purpose in the first sentence, followed by usage guidelines in a clear, efficient manner. Every sentence adds value without redundancy, making it appropriately sized and well-structured for quick comprehension.
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 low complexity (0 parameters, no output schema), the description is complete enough for effective use. It covers purpose, usage guidelines, and behavioral context. The absence of an output schema is mitigated by the description's clarity on what is returned ('the latest picture'), though more detail on output format could enhance completeness.
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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, maintaining focus on tool functionality. A baseline of 4 is applied since there are no parameters to document.
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 ('Gets the latest picture from the webcam') and resource ('webcam'), distinguishing it from the sibling tool 'screenshot' which likely captures screen content rather than camera input. The verb 'Gets' is precise and the resource is 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 explicitly provides when-to-use guidance with concrete examples: 'if the human asks questions about their immediate environment,' 'if you want to see the human,' or 'to examine an object they may be referring to or showing you.' This gives clear context for selecting this tool over alternatives like 'screenshot'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
screenshotBRead-only
Gets a screenshot of the current screen or window
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, non-destructive operation with open-world assumptions. The description adds minimal behavioral context beyond this, such as specifying it captures the 'current screen or window', but doesn't detail aspects like format, size, or potential limitations. No contradiction with annotations exists.
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, clear sentence that efficiently conveys the core functionality without any wasted words. It is front-loaded with the essential information, making it easy for an agent to parse and understand quickly.
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 simplicity (0 parameters, no output schema) and rich annotations (readOnlyHint, openWorldHint), the description is adequate but minimal. It covers the basic purpose but lacks details on output format or behavioral nuances that could aid the agent, such as whether it returns an image file or data. It meets minimum viability but has gaps in completeness.
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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's action. A baseline of 4 is applied since it avoids redundancy and adds value by explaining the tool's purpose without unnecessary details.
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 a specific verb ('Gets') and resource ('screenshot of the current screen or window'), making it immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'capture', which might have overlapping functionality, preventing a perfect score.
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 the sibling tool 'capture', nor does it mention any prerequisites, context, or exclusions. It merely states what the tool does without offering usage instructions, leaving the agent to infer when it's appropriate.
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. Dates show when Glama detected each change.
2 tool updates
v1.0.0- First observed
capture - First observed
screenshot
TDQS
The tools have overlapping purposes—both capture visual data from the user's environment—with 'capture' targeting the webcam and 'screenshot' targeting the screen, but descriptions could lead to confusion as 'capture' mentions examining objects the human shows, which might overlap with screen content. The boundaries are somewhat unclear, especially for agents interpreting use cases.
Tool names follow a consistent verb-based pattern ('capture' and 'screenshot'), both being single words describing the action. There are no deviations in style or casing, making them readable and predictable, though 'screenshot' is more specific than 'capture' in terms of naming convention.
With only 2 tools, the server feels under-scoped for a webcam domain, as it lacks operations like video capture, settings adjustment, or multi-camera support. This minimal set may limit agent functionality, making it borderline too few for comprehensive visual input handling.
The tool surface is significantly incomplete for a webcam server; it covers basic image capture from webcam and screen but misses essential operations such as starting/stopping video, configuring camera settings, or handling multiple inputs. This creates gaps that could lead to agent failures in more complex visual tasks.
Maintenance
Resources
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