MCP server
by evalstate
Capture live webcam images or screenshots easily. Supports screenshot on Mac, screen snip on Mac, and screenshot screen
Provides AI assistants access to your webcam and screen captures for visual context about your environment and current activity.
Webcam/Screenshot Capture is a community-built MCP server published by evalstate that provides AI assistants with tools and capabilities via the Model Context Protocol. Capture live webcam images or screenshots easily. Supports screenshot on Mac, screen snip on Mac, and screenshot screen It is categorized under developer tools. This server exposes 2 tools that AI clients can invoke during conversations and coding sessions.
You can install Webcam/Screenshot Capture in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Webcam/Screenshot Capture is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We wired Webcam/Screenshot Capture into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
I recommend Webcam/Screenshot Capture for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Webcam/Screenshot Capture benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Webcam/Screenshot Capture is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: Webcam/Screenshot Capture surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: Webcam/Screenshot Capture is the kind of server we cite when onboarding engineers to host + tool permissions.
Useful MCP listing: Webcam/Screenshot Capture is the kind of server we cite when onboarding engineers to host + tool permissions.
Webcam/Screenshot Capture is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Webcam/Screenshot Capture is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Webcam/Screenshot Capture is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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In celebration of getting 52 GitHub stars, mcp-webcam 0.2.0 is here! Now supports streamable-http!! No installation required! - try it now at https://webcam.fast-agent.ai/. You can specify your own UserID by adding ?user=<YOUR_USER_ID> after the URL. Note this shared instance is for fun, not security - see below for instructions how to run your own copy locally.
In streamable-http mode multiple clients can connect simultaneously, and you can choose which is used for Sampling.
If we get to 100 stars I'll add another feature 😊.
When run in Streaming mode, if you set an MCP_HOST environment variable the host name is used as a prefix in URL construction, and 5 character UserIDs are automatically generated when the User lands on the webpage.
MCP Server that provides access to your WebCam. Provides capture and screenshot tools to take an image from the Webcam, or take a screenshot. The current image is also available as a Resource.
mcp-webcam supports "sampling"! Press the "Sample" button to send a sampling request to the Client along with your entered message.
[!TIP] Claude Desktop does not currently support Sampling. If you want a Client that can handle multi-modal sampling request, try https://github.com/evalstate/fast-agent/ or VSCode (more details below).
Install a recent version of NodeJS for your platform. The NPM package is @llmindset/mcp-webcam.
To start in STDIO mode: npx @llmindset/mcp-webcam. This starts the mcp-webcam UI on port 3333. Point your browser at http://localhost:3333 to get started.
To change the port: npx @llmindset/mcp-webcam 9999. This starts mcp-webcam the UI on port 9999.
For Streaming HTTP mode: npx @llmindset/mcp-webcam --streaming. This will make the UI available at http://localhost:3333 and the MCP Server available at http://localhost:3333/mcp.
You can run mcp-webcam using Docker. By default, it starts in streaming mode:
docker run -p 3333:3333 ghcr.io/evalstate/mcp-webcam:latest
MCP_TRANSPORT_MODE - Set to stdio for STDIO mode, defaults to streamingPORT - The port to run on (default: 3333)BIND_HOST - Network interface to bind the server to (default: localhost)MCP_HOST - Public-facing URL for user instructions and MCP client connections (default: http://localhost:3333)# STDIO mode
docker run -p 3333:3333 -e MCP_TRANSPORT_MODE=stdio ghcr.io/evalstate/mcp-webcam:latest
# Custom port
docker run -p 8080:8080 -e PORT=8080 ghcr.io/evalstate/mcp-webcam:latest
# For cloud deployments with custom domain (e.g., Hugging Face Spaces)
docker run -p 3333:3333 -e MCP_HOST=https://evalstate-mcp-webcam.hf.space ghcr.io/evalstate/mcp-webcam:latest
# Complete cloud deployment example
docker run -p 3333:3333 -e MCP_HOST=https://your-domain.com ghcr.io/evalstate/mcp-webcam:latest
If you want a Client that supports sampling try:
Start the mcp-webcam in streaming mode, install uv and connect with:
uvx fast-agent-mcp go --url http://localhost:3333/mcp
fast-agent currently uses Haiku as its default model, so set an ANTHROPIC_API_KEY. If you want to use a different model, you can add --model on the command line. More instructions for installation and configuration are available here: https://fast-agent.ai/models/.
To start the server in STDIO mode, add the following to your fastagent.config.yaml
webcam_local:
command: "npx"
args: ["@llmindset/mcp-webcam"]
VSCode versions 1.101.0 and above support MCP Sampling. Simply start mcp-webcam in streaming mode, and add http://localhost:3333/mcp as an MCP Server to get started.
Claude Desktop does NOT support Sampling. To run mcp-webcam from Claude Desktop, add the following to the mcpServers section of your claude_desktop_config.json file:
"webcam": {
"command": "npx",
"args": [
"-y",
"@llmindset/mcp-webcam"
]
}
Start Claude Desktop, and connect to http://localhost:3333. You can then ask Claude to get the latest picture from my webcam, or Claude, take a look at what I'm holding or what colour top am i wearing?. You can "freeze" the current image and that will be returned to Claude rather than a live capture.
You can ask for Screenshots - navigate to the browser so that you can guide the capture area when the request comes in. Screenshots are automatically resized to be manageable for Claude (useful if you have a 4K Screen). The button is there to allow testing of your platform specific Screenshot UX - it doesn't do anything other than prepare you for a Claude intiated request. NB this does not not work on Safari as it requires human initiation.
That's it really.
This MCP Server was built to demonstrate exposing a User Interface on an MCP Server, and serving live resources back to Claude Desktop.
This project might prove useful if you want to build a local, interactive MCP Server.
Thanks to https://github.com/tadasant for help with testing and setup.
Please read the article at https://llmindset.co.uk/posts/2025/01/resouce-handling-mcp for more details about handling files and resources in LLM / MCP Chat Applications, and why you might want to do this.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.