How to Use the Elasticsearch Vector MCP in Windsurf

Q: Can Windsurf use Elasticsearch Vector to find similar documents?

Yes. It uses the search tool to perform kNN queries against your indices. Just pass your embedding, and the agent returns the closest matches.

Q: How do I manage Elasticsearch Vector mappings in Windsurf?

You use createindex to define your schema. This allows you to set vector dimensions and data types directly from your chat session.

Use Windsurf to run kNN searches directly against your Elasticsearch cluster, keeping your vector data and app code in one place.

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Connect Elasticsearch Vector MCP to Windsurf

Create your Vinkius account to connect Elasticsearch Vector 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 Elasticsearch Vector with Windsurf

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Autonomous vector index management

Cascade handles your index lifecycle without manual intervention. Use `create_index` to define your vector dimensions and mapping, then verify status with `get_index`. Windsurf reads your existing cluster state to ensure your indices match your embedding model requirements. It manages the setup so you can focus on building features.

Direct document indexing in Windsurf

Feed your processed embeddings straight into Elasticsearch with `index_document`. The agent handles the JSON structure and ensures your vectors land in the right shards. Delete outdated records instantly using `delete_document` to keep your search relevance high. It keeps your vector store clean as your data evolves.

Perform kNN searches with Windsurf

Execute high-performance vector searches using the `search` tool. Cascade takes your query vector and retrieves the most relevant documents in milliseconds. This MCP server gives your agent the ability to pull context from your production data. You get exact results without switching between your IDE and a database dashboard.

Setup guide

Set up Elasticsearch Vector 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 Elasticsearch Vector 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 Elasticsearch Vector payment history." If connected, Cascade will call the Elasticsearch Vector tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.json

{
  "mcpServers": {
    "elasticsearch-vector-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Elasticsearch Vector. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Elasticsearch Vector MCP in Windsurf

Yes. It uses the `search` tool to perform kNN queries against your indices. Just pass your embedding, and the agent returns the closest matches.

It does. As long as you have the proper credentials, you can point this server at your current cluster. Use `list_indexes` to see what is already there.

You use `create_index` to define your schema. This allows you to set vector dimensions and data types directly from your chat session.

Vinkius handles authentication via a single endpoint token. Your data stays within your authorized infrastructure and is only accessed when the agent invokes a tool.

This server interacts with your Elasticsearch index metadata, raw vector embeddings, and document fields. It only reads or writes what you explicitly authorize through the tool calls.

Use it with your favorite AI tools

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