How to Use the Clarifai (Vision AI) MCP in Claude Code

Q: How do I add Clarifai (Vision AI) to Claude Code?

Run claude mcp add --transport http clarifai-vision-ai-mcp -- . Ensure you place all transport flags before the server name. Verify the connection with claude mcp list.

Q: How does the CLI handle complex Clarifai (Vision AI) workflows?

The agent uses listworkflows to retrieve the exact structural matching for your environment. This verifies your chained AI limits directly in the terminal output.

Q: Can I list my custom models via the command line?

Your terminal agent calls listmodels to fetch them. It extracts the computer vision parameters and prints the available model IDs so you can pipe them to other tools.

Run Clarifai (Vision AI) inferences and manage computer vision models straight from your terminal with Claude Code.

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Connect Clarifai (Vision AI) MCP to Claude Code

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Setup Clarifai (Vision AI) with Claude Code

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Audit environments with the MCP Server

Managing computer vision infrastructure from the command line requires direct access to your environments. Claude Code runs `list_apps` to identify the bounded applications managing your global compute limits. You get the exact IDs piped right to stdout. Need to verify what is currently deployed? The CLI calls `list_models` to perform structural extraction of the parameters driving your AI features. You can script this into a cron job to monitor model availability without ever opening a browser.

Query datasets and concepts

CI/CD pipelines often need to validate dataset integrity before triggering a new training run. Using `list_datasets`, your terminal agent identifies precise physical bounds mapping your data structures. It resolves the visual nodes programmatically. To check the tagging limits, Claude Code executes `list_concepts`. This extracts the explicitly attached semantic bounds tagging your datasets. You can pipe those text outputs directly into your validation scripts.

Execute predictions in CI/CD

You can test model endpoints directly during your build process. Claude Code fires `predict_model` to dispatch an automated validation inference. It routes explicit network predictions and grabs the response payload. The terminal agent parses the evaluated bounding image classifications instantly. If the prediction fails or returns unexpected confidence scores, your shell script can halt the deployment automatically.

Setup guide

Set up Clarifai (Vision AI) MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see clarifai-vision-ai-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Clarifai (Vision AI) transactions." It will automatically discover and invoke the available Clarifai (Vision AI) tools.

Terminal

claude mcp add --transport http clarifai-vision-ai-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

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Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Clarifai (Vision AI) MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

{
  "mcpServers": {
    "clarifai-vision-ai-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about Clarifai (Vision AI) MCP in Claude Code

Run `claude mcp add --transport http clarifai-vision-ai-mcp -- `. Ensure you place all transport flags before the server name. Verify the connection with `claude mcp list`.

Yes. You can configure the CLI to run headless within your CI runner. It will execute `predict_model` to validate your computer vision endpoints before merging PRs.

The agent uses `list_workflows` to retrieve the exact structural matching for your environment. This verifies your chained AI limits directly in the terminal output.

Your terminal agent calls `list_models` to fetch them. It extracts the computer vision parameters and prints the available model IDs so you can pipe them to other tools.

Vinkius hosts this MCP Server in an ephemeral, zero-trust sandbox. When your terminal sends a payload to `predict_model`, the isolated container processes the network prediction and terminates immediately, leaving no trace of your visual data.

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