How to Use the EyePop.ai MCP in VS Code Copilot

Q: How do we share EyePop.ai configurations in VS Code Copilot?

Commit a .vscode/mcp.json file to your shared repository. This lets every developer on your team access the EyePop.ai tools in VS Code Copilot using this MCP integration.

Q: Can we run image classification tests inside VS Code Copilot?

Yes. Run analyzeimage to send local images to your EyePop.ai models. VS Code Copilot displays the classification results and bounding boxes directly in your workspace terminal.

Q: How do we check our EyePop.ai service health in VS Code Copilot?

Execute checkeyepopstatus directly in VS Code Copilot to check API availability. This ensures your team's development environments are properly connected to the active endpoints.

Q: Can VS Code Copilot help us build new EyePop.ai pipelines?

Yes. Run createpop within VS Code Copilot to configure a new visual pipeline. The agent sets up the configuration on your account via the MCP interface, making it immediately available for your codebase.

Share standardized EyePop.ai computer vision tools across your team using VS Code Copilot and shared workspace configurations.

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Connect EyePop.ai MCP to VS Code Copilot

Create your Vinkius account to connect EyePop.ai to VS Code Copilot and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup EyePop.ai with VS Code Copilot

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Run EyePop.ai pipelines in VS Code Copilot

The `list_pops` tool retrieves all active visual pipelines from your team's EyePop.ai account directly into VS Code Copilot. This keeps every developer aligned on which production pipelines are available for integration via the EyePop.ai MCP Server. When building new features, your team uses `get_pop` to inspect specific pipeline structures. VS Code Copilot reads this configuration to recommend matching code patterns across different files in your workspace.

Analyze video streams in team workspaces

The `analyze_video` tool processes temporal media directly through VS Code Copilot to extract object coordinates over time. Your agent uses this tracking data to help you write media playback and overlay features. If a video pipeline fails, you run `check_eyepop_status` to determine whether the issue lies in your local workspace or the remote API. This diagnostic step saves your engineering team hours of manual debugging.

Inspect model metadata directly in the editor

The `get_model` tool pulls detailed schemas for specific computer vision models inside VS Code Copilot. Your agent reads these parameters to generate typed interfaces for your frontend and backend code. You use `list_models` to discover new classification or detection capabilities. This allows your team to expand application features without manually searching through external API documentation.

Setup guide

Set up EyePop.ai MCP in VS Code Copilot

Prerequisites

VS Code 1.99 or later with GitHub Copilot extension
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and run "MCP: Add Server". Select HTTP (Streamable) as the server type. VS Code will create .vscode/mcp.json in your workspace.
2

Add the EyePop.ai MCP

Paste the JSON snippet shown on the right into your .vscode/mcp.json. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Switch to Agent mode

Open Copilot Chat (Cmd+Shift+I / Ctrl+Shift+I) and switch to Agent mode using the dropdown. MCP tools are only available in Agent mode — they do not appear in Edit or Ask modes.
4

Verify the connection

In the Copilot Chat input, type # to list available tools. You should see the EyePop.ai tools listed. Try asking: "List my recent EyePop.ai transactions" and Copilot will invoke them automatically.

.vscode/mcp.json

{
  "mcpServers": {
    "eyepopai-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by EyePop.ai. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about EyePop.ai MCP in VS Code Copilot

Commit a `.vscode/mcp.json` file to your shared repository. This lets every developer on your team access the EyePop.ai tools in VS Code Copilot using this MCP integration.

Yes. Run `analyze_image` to send local images to your EyePop.ai models. VS Code Copilot displays the classification results and bounding boxes directly in your workspace terminal.

Execute `check_eyepop_status` directly in VS Code Copilot to check API availability. This ensures your team's development environments are properly connected to the active endpoints.

Yes. Run `create_pop` within VS Code Copilot to configure a new visual pipeline. The agent sets up the configuration on your account via the MCP interface, making it immediately available for your codebase.

Your images, video streams, and detection metadata are routed directly to your EyePop.ai account. VS Code Copilot acts as a client, using a secure MCP bridge that never logs, caches, or exposes your media assets to external third parties.

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