How to Use the Voyage AI (AI Embeddings API) MCP in Cursor

Q: How do I use Voyage AI (AI Embeddings API) with Cursor?

You enable it by setting up the server in your project's .cursor/mcp.json file and activating Agent mode in chat. The AI then uses these tools to generate live code using real responses.

Q: Can Voyage AI (AI Embeddings API) handle multimodal data in Cursor?

Yep. Use createmultimodalembeddings when you need the agent to understand relationships between different input types—say, an image and a corresponding function signature. The resulting vectors are usable right inside your code.

Q: How do I manage my data when using Voyage AI (AI Embeddings API) with Cursor?

The agent uses listfiles and getfilecontent to get context. If you need to clean up, the server provides a deletefile function, giving you full control over your project's data source.

Live embeddings and RAG context injected directly into your code with Cursor.

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Connect Voyage AI (AI Embeddings API) MCP to Cursor

Create your Vinkius account to connect Voyage AI (AI Embeddings API) 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 Voyage AI (AI Embeddings API) with Cursor

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Generating Contextual Embeddings for Code

The `create_contextualized_embeddings` tool lets the AI generate vector representations of specific document chunks. This is perfect when you need to embed only a section of code or documentation, not an entire file. When your agent calls this function, it injects the resulting embedding data directly into the code block you're writing, giving you live context without ever leaving Cursor.

High-Precision Document Reranking

Don't rely on basic similarity scores. Use `rerank` to refine retrieved documents against a query. This function takes the raw results and reorders them by actual relevance, which is vital for robust coding context. Cursor injects this highly relevant data directly into your working code. It means when you ask the AI to fix something based on documentation, it's using the *right* section of that docs.

Batch Processing and File Management

For large projects, run `create_batch` via your agent mode. This allows you to process many files without blocking your coding flow. You'll manage the job status using `get_batch`, monitoring it right alongside writing code. The ability to upload files for batch inference (`upload_file`) means you can feed a whole repository of documentation into the system and start getting embeddings immediately.

Setup guide

Set up Voyage AI (AI Embeddings API) 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 Voyage AI (AI Embeddings API) 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 Voyage AI (AI Embeddings API) transactions." If the MCP tools are loaded correctly, Cursor will call the Voyage AI (AI Embeddings API) tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "voyage-ai-ai-embeddings-api-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Voyage AI (AI Embeddings API) MCP in Cursor

You enable it by setting up the server in your project's `.cursor/mcp.json` file and activating Agent mode in chat. The AI then uses these tools to generate live code using real responses.

Yep. Use `create_multimodal_embeddings` when you need the agent to understand relationships between different input types—say, an image and a corresponding function signature. The resulting vectors are usable right inside your code.

Your code context is paramount here. The MCP server operates in the project scope, meaning sensitive information stays within your local development environment and never leaves your editor's control.

N/A. Cursor operates in the project scope, making it ideal for local development without needing remote connectivity or web URLs.

The agent uses `list_files` and `get_file_content` to get context. If you need to clean up, the server provides a `delete_file` function, giving you full control over your project's data source.

Use it with your favorite AI tools

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