How to Use the DeepInfra (Serverless LLM Inference) MCP in Cursor

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Connect DeepInfra (Serverless LLM Inference) MCP to Cursor

Create your Vinkius account to connect DeepInfra (Serverless LLM Inference) to Cursor 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 DeepInfra (Serverless LLM Inference) with Cursor

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Test inference logic inside Cursor

The `create_chat_completion` tool lets the editor ping remote models while you write the implementation code. You specify the target identifier, and Agent mode pulls the actual response back into your open file. Writing API wrappers becomes trivial when the editor tests the endpoint for you. The agent sees the real JSON structure and formats your interfaces to match exactly.

Build vector pipelines with real data

Calling the `create_embedding` tool generates actual float arrays from your text strings right in the IDE workspace. You highlight a block of text, ask the agent to embed it, and watch the numbers populate. Testing semantic search features requires real vectors, not mocked arrays. The MCP Server fetches the exact output format you will see in production.

Prototype complex AI features

The `run_native_inference` tool executes non-standard requests like audio processing or OCR directly from your project environment. Your agent constructs the payload and fires it off without requiring a separate terminal window. Triggering the `generate_image` operation gives you immediate visual feedback on your prompt engineering. You inspect the resulting asset before committing the string to your source code.

Setup guide

Set up DeepInfra (Serverless LLM Inference) MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the DeepInfra (Serverless LLM Inference) MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent DeepInfra (Serverless LLM Inference) transactions." If the MCP tools are loaded correctly, Cursor will call the DeepInfra (Serverless LLM Inference) tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "deepinfra-serverless-llm-inference-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about DeepInfra (Serverless LLM Inference) MCP in Cursor

Create an mcp.json file in your project root directory. Add the server details to the mcpServers object and enable Agent mode in your chat panel.

Yes. The editor fetches real vector arrays via the tool and writes the exact typing interfaces you need. You stop guessing what the API response looks like.

The inference happens remotely, but the integration feels local. Your agent interacts with the serverless endpoints as if they were running on your own machine.

Yes. The native inference tool handles payloads that standard chat endpoints reject. You pass custom JSON for speech or video tasks and inspect the results immediately.

The server only receives the specific string arrays or text prompts you target for inference. Your broader project files remain secure because the zero-trust Vinkius architecture isolates every single tool execution.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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