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

Q: Does Voyage AI (AI Embeddings API) support batch processing with Windsurf?

Yep. You can queue up large jobs using createbatch. The MCP Server manages the lifecycle for you, letting you track progress via getbatch until your embeddings are ready.

Q: Can I use Voyage AI (AI Embeddings API) to improve my code flow in Windsurf?

Absolutely. You can run rerank within your workflow. This takes a query and compares it against existing documents, giving you highly relevant context for whatever component or script you're building.

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Connect Voyage AI (AI Embeddings API) MCP to Windsurf

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

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Claude

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Build RAG workflows automatically

When you set a goal, Cascade handles the heavy lifting. It uses `upload_file` to stage documents for batch processing and then calls `create_contextualized_embeddings` to generate chunk embeddings. Cascade doesn't stop there. It can run `rerank` against those new document chunks, giving you high-precision results without manual scripting.

Handle massive data sets in batches

The agent manages large inference jobs for you. You start a job with `create_batch`, and Cascade tracks its status using `get_batch`. If something goes wrong, it's easy to call `cancel_batch` or check the list via `list_batches`. This makes running huge data pipelines reliable. It’s designed for multi-step actions—just tell Windsurf what you need.

Manage and inspect files easily

Need to see what's available? The agent lists all stored assets with `list_files` or grabs metadata using `get_file`. If you need the raw data, it downloads content via `get_file_content`. It also handles cleanup. You can delete files that are no longer needed in your project by calling `delete_file`.

Setup guide

Set up Voyage AI (AI Embeddings API) MCP in Windsurf

Prerequisites

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

1

Open MCP configuration

Click the Cascade assistant icon in the sidebar, then click the hammer icon (🔨) at the top of the panel. Select "Configure" to open ~/.codeium/windsurf/mcp_config.json.
2

Add the Voyage AI (AI Embeddings API) 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

Refresh MCPs

Go back to the hammer icon (🔨) in Cascade and click "Refresh". Windsurf will detect the new server. No full restart is needed — the connection is hot-reloaded.
4

Verify in Cascade

Start a new Cascade conversation and ask something like "Show my Voyage AI (AI Embeddings API) payment history." If connected, Cascade will call the Voyage AI (AI Embeddings API) tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.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 Windsurf

You'll use the agent to initiate multiple steps. Start by calling `create_embeddings` for simple text, or use `create_multimodal_embeddings` if your source material has images. Cascade makes sure these calls are chained correctly.

Yep. You can queue up large jobs using `create_batch`. The MCP Server manages the lifecycle for you, letting you track progress via `get_batch` until your embeddings are ready.

The server touches file metadata and the content of uploaded files. Specifically, you deal with raw document text and file identifiers when generating embeddings.

Absolutely. You can run `rerank` within your workflow. This takes a query and compares it against existing documents, giving you highly relevant context for whatever component or script you're building.

It is built to be reliable. The agent handles multi-step execution, which means you don't have to worry about manually prompting every time a tool needs to run or check its status.

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