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

Q: How do I generate embeddings using Voyage AI (AI Embeddings API) with Cline?

You can ask Cline to create simple text vectors via createembeddings or run complex, chunk-aware embedding jobs by calling createcontextualizedembeddings. The agent manages the input data for you.

Q: Can I use Voyage AI (AI Embeddings API) with Cline to improve my code?

Yes. You can prompt Cline to run a reranking step using rerank against existing documents, ensuring your code uses the most relevant context available.

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

It does. You initialize a job via createbatch. The MCP Server manages this background process, and you can track its progress using listbatches or getbatch.

Execute full RAG pipelines and file operations with Cline’s coding agent.

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Connect Voyage AI (AI Embeddings API) MCP to Cline

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

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Run end-to-end batch inference

Tell Cline to process a dataset, and it runs the job. You use `upload_file` for batch input, then trigger `create_batch`. The agent keeps an eye on the status using `get_batch` until all your embeddings are finished. This capability lets you treat large data transformations like writing code—just tell Cline what to do.

Create contextualized and multimodal vectors

Need more than basic text embeddings? You can generate specialized vectors using `create_contextualized_embeddings` or handle mixed media with `create_multimodal_embeddings`. These are crucial for advanced RAG systems. Cline handles the setup, letting you focus on what those embeddings *do* in your component.

Manage files and data assets

The agent makes file handling simple. It lists all available resources with `list_files` or downloads content using `get_file_content`. You can also clean up by calling `delete_file` on unused assets. It's like having a built-in Git for your data, right inside the IDE.

Setup guide

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

Prerequisites

VS Code with Cline extension installed
Active Vinkius subscription with a valid endpoint token

1

Open Cline MCP settings

Click the Cline icon in the VS Code sidebar to open the Cline panel. Then click the MCP Servers icon (server stack) at the top-right corner of the panel.
2

Add a remote server

Click "Remote Servers" at the top, then click "Add Remote MCP". In the Name field, type voyage-ai-ai-embeddings-api-mcp. In the URL field, paste your Vinkius endpoint: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp. Get your token from cloud.vinkius.com.
3

Enable the server

After saving, the server appears in the Cline MCP panel. Toggle the switch to enable it. The status indicator turns green when the connection is live.
4

Start using tools

Return to the Cline chat and ask: "Check my latest Voyage AI (AI Embeddings API) refund status." Cline will discover the available tools and request your approval before invoking each one — giving you full control over every action.

Cline MCP Settings

{
  "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 Cline

You can ask Cline to create simple text vectors via `create_embeddings` or run complex, chunk-aware embedding jobs by calling `create_contextualized_embeddings`. The agent manages the input data for you.

Yes. You can prompt Cline to run a reranking step using `rerank` against existing documents, ensuring your code uses the most relevant context available.

It does. You initialize a job via `create_batch`. The MCP Server manages this background process, and you can track its progress using `list_batches` or `get_batch`.

The server touches raw file content and metadata. When creating embeddings, you're dealing specifically with text chunks or multimodal inputs that need vectorization.

It’s built for end-to-end execution. Cline executes multi-step processes—from data retrieval to embedding creation and then component generation—all without manual intervention.

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