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

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

In your shell script, you can call createembeddings for basic vectors or use createmultimodalembeddings if the data includes images. The server exposes these functions via an HTTP endpoint.

Q: Can I use Voyage AI (AI Embeddings API) with Claude Code to improve my pipeline?

Yes. You can run a rerank step in your script. This takes query data and improves the relevance of documents, which is essential for robust RAG pipelines.

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

It supports it. Your script initiates a job by calling createbatch. The MCP Server manages the state, allowing your pipeline to check status using getbatch.

Embeddings, RAG, and file ops for CI/CD pipelines with Claude Code.

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Setup Voyage AI (AI Embeddings API) with Claude Code

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Process data in headless batches

When running a pipeline script, you need massive embeddings. Use `upload_file` to stage the source data, then call `create_batch`. The MCP Server manages this job entirely via background processes. You check status using `get_batch` or view all jobs with `list_batches`. Perfect for cron jobs.

Generate specialized vectors programmatically

The script can call multiple embedding tools: simple text conversion via `create_embeddings`, deep context handling using `create_contextualized_embeddings`, or multimodal input processing with `create_multimodal_embeddings`. This gives your CI/CD job maximum flexibility for different data types.

Manage and inspect remote files

If the script needs to work on external assets, it can list them with `list_files`, grab metadata using `get_file`, or pull raw content via `get_file_content`. Cleanup is simple: call `delete_file` when the job finishes. The MCP Server handles all file operations.

Setup guide

Set up Voyage AI (AI Embeddings API) 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 voyage-ai-ai-embeddings-api-mcp with a green status indicator.
3

Start using tools

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

Terminal

claude mcp add --transport http voyage-ai-ai-embeddings-api-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 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

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

Get your connection token →

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Common questions about Voyage AI (AI Embeddings API) MCP in Claude Code

In your shell script, you can call `create_embeddings` for basic vectors or use `create_multimodal_embeddings` if the data includes images. The server exposes these functions via an HTTP endpoint.

Yes. You can run a `rerank` step in your script. This takes query data and improves the relevance of documents, which is essential for robust RAG pipelines.

It supports it. Your script initiates a job by calling `create_batch`. The MCP Server manages the state, allowing your pipeline to check status using `get_batch`.

The server touches file content and metadata. When processing embeddings, the script interacts with raw text or binary data that needs to be vectorized.

It's designed for headless execution. Since it runs via HTTP, your scripts can reliably call tools like `list_batches` and process large jobs without needing a graphical interface.

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