Building Computer Vision Applications with the Groundlight MCP Server

Picture this: You need to build a computer vision application that monitors your hummingbird feeder and sends you photos whenever these tiny visitors arrive. In the traditional world of machine learning, you'd be looking at weeks of work—wrestling with PyTorch, annotating training data, and building custom alert systems. But what if you could create this entire system just by having a conversation?

With the new Groundlight MCP (Model Context Protocol) server, sophisticated visual monitoring systems are now as easy to build as describing what you want in plain English.

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The power of conversational development with LLMs

The Model Context Protocol represents a fundamental shift in how we interact with APIs and services. Rather than diving into documentation and writing glue code, MCP creates a bridge between natural language and complex systems.

When you combine Claude or another LLM with Groundlight's managed computer vision platform via an MCP server, complex visual analysis tasks that once required deep ML expertise and application programming experience become accessible to anyone who can describe what they're looking for.

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A real-world example: The Hummingbird Monitor

Let me show you what this looks like in practice. A user recently wanted to monitor their hummingbird feeder with automatic alerts. Here's how the entire system came together through a simple conversation:

The journey began with a straightforward request: "I have a camera available over RTSP at rtsp://my-camera-url. Can you capture an image from the stream?"

Within seconds, the assistant had set up a framegrabber and captured a test image. No wrestling with RTSP protocols or video codecs—just immediate verification that the camera connection worked.

Next came the core functionality: "I want to use this stream to count the number of hummingbirds at my feeder."

Behind the scenes, the MCP server orchestrated a sophisticated solution. It created two complementary detectors—a lightweight binary detector for rapid presence detection and a more sophisticated counting detector that provides accurate counts with bounding boxes. This two-stage approach balances speed with accuracy, a pattern that would typically require significant expertise to implement correctly.

The alert system came together just as naturally: "Create an alert if there are any hummingbirds and text me."

What would normally involve configuring SMS gateways, managing notification queues, and handling rate limiting became a single conversational request. Claude simply tells the Groundlight service to create an alert that will send a text alert if a hummingbird is detected, and it does!

Finally, the user asked for a complete application: "Write a Python project that utilizes this setup."

The result? A production-ready monitoring system that implements motion detection as a pre-filter to reduce API costs, manages detection state intelligently to avoid duplicate alerts, handles network failures gracefully, and logs all activity for later analysis. These aren't just nice-to-have features—they're the difference between a proof-of-concept and a system you can actually rely on.

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The deeper implications

What makes this approach so powerful isn't just the time saved—though reducing a few days of work to minutes is certainly compelling. It's how the Groundlight MCP server ensures best practices are baked into every application.

When you ask for a detection system, you automatically get confidence threshold management. When you request alerts, you get intelligent cooldown periods to prevent notification fatigue. When you need image analysis, you get a pipeline optimized for both accuracy and cost. These patterns, learned from thousands of real-world deployments, become part of your application automatically without you having to discover them through trial and error.

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Getting started with the Groundlight MCP server

Setting up the Groundlight MCP server is refreshingly simple. If you're using Claude Desktop (currently the best client for MCP servers), you just need to update your configuration file:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/<your-username>/Desktop"
      ]
    },
    "framegrab": {
      "command": "uvx",
      "args": ["framegrab-mcp-server"]
    },
    "groundlight": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "GROUNDLIGHT_API_TOKEN",
        "groundlight/groundlight-mcp-server"
      ],
      "env": {
        "GROUNDLIGHT_API_TOKEN": "YOUR_API_TOKEN_HERE"
      }
    }
  }
}

This configuration sets up three complementary servers: filesystem access for reading and writing files, framegrab for capturing images from cameras, and Groundlight for the computer vision magic. Together, they provide everything you need to build sophisticated visual applications through conversation.

You will need a Groundlight API token to use this service. Sign up for a free account by clicking the Login button at groundlight.ai and generate an API token by following the link under your account management menuThen use the generated token in the Claude Desktop configuration file above. 

We have made the groundlight-mcp-server public under the Apache-2.0 license for local development and community contributions.

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The future of software development

The Groundlight MCP server represents more than just a new tool — it's a glimpse into the future of software development. As AI assistants become more capable and protocols like MCP mature, we're moving toward a world where describing what you want to build is the same as building it.

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Read more of our take on computer vision via MCP here. 

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Appendix

Prompts for running the demo with Claude Desktop (Used Claude 4 Sonnet):

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I have a camera available over RTSP at rtsp://admin:Orangeiris825\!@192.168.0.203:554/cam/realmonitor?channel=1&subtype=0. Can you use framegrab to capture an image from the stream?
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I want to use this stream to count the number of hummingbirds at my hummingbird feeder. Create groundlight detectors and then write and save a python project that will utilize that. Follow Groundlight’s documentation for building applications. Create an alert using Groundlight’s alert system to send me a text message if there are any hummingbirds.