How to Use the GPTZero MCP in VS Code Copilot

Q: How do we share the GPTZero config across our team in VS Code Copilot?

You can commit an mcp.json file inside your .vscode folder. This configures the MCP Server for every developer on the project, allowing their agent to call detectaiintext instantly.

Q: Can we monitor our GPTZero API limits within VS Code Copilot?

Yes, developers can ask the agent to run getapiquotas to check remaining credits. This keeps your team aware of usage before running large batch scans.

Q: How do we verify that the integration is online?

Run checkapihealth to verify connectivity. It returns a quick diagnostic status to confirm the API is reachable.

Verify the authenticity of your project documentation and manage API keys inside VS Code Copilot.

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Connect GPTZero MCP to VS Code Copilot

Create your Vinkius account to connect GPTZero 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 GPTZero with VS Code Copilot

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Audit document origin directly in VS Code Copilot

The `detect_ai_in_text` tool analyzes your workspace files for AI-generated text patterns and returns sentence-level highlights. Your agent can run this check across entire directories during code reviews or documentation updates. This ensures your team's technical writing and public-facing guides remain completely authentic. It eliminates the friction of manual copy-pasting.

Monitor team quotas and account status

The `get_current_user` tool retrieves the profile details of the active MCP Server user. This helps teams sharing a single API key keep track of who is running scans and which subscription tier is active. You can also call `get_api_quotas` to verify remaining credits. It prevents your CI/CD pipelines or automated review scripts from breaking due to over-limit errors.

Manage this MCP Server across your engineering team

The `get_usage_policy` tool provides clear boundaries on rate limits for this MCP setup. This allows your team to configure shared workspace files without triggering API bans or blocks. If a developer runs into an ambiguous result, they can call `get_interpretation_guide` to read the official score guidelines. It keeps everyone on the same page regarding detection accuracy.

Setup guide

Set up GPTZero 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 GPTZero 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 GPTZero tools listed. Try asking: "List my recent GPTZero transactions" and Copilot will invoke them automatically.

.vscode/mcp.json

{
  "mcpServers": {
    "gptzero-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 GPTZero. 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 GPTZero MCP in VS Code Copilot

You can commit an `mcp.json` file inside your `.vscode` folder. This configures the MCP Server for every developer on the project, allowing their agent to call `detect_ai_in_text` instantly.

Yes, developers can ask the agent to run `get_api_quotas` to check remaining credits. This keeps your team aware of usage before running large batch scans.

If a human-written document gets flagged, your agent can run `submit_prediction_feedback` to report the error. This sends the correction back to improve the detection model.

Run `check_api_health` to verify connectivity. It returns a quick diagnostic status to confirm the API is reachable.

Text submitted for analysis is sent directly to the API over encrypted connections. Running this MCP Server on Vinkius ensures that your proprietary documentation and prose are processed in an ephemeral sandbox, leaving zero persistent storage footprint.

Use it with your favorite AI tools

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