How to Use the Elasticsearch Vector MCP in Claude

Q: How do I configure this Elasticsearch Vector MCP Server in Claude Desktop?

Open your claudedesktopconfig.json file and add the server to your mcpServers list. Provide your cluster endpoint and credentials as environment variables. Once you restart the app, the hammer icon will display the six vector search tools.

Q: Can Claude Desktop handle 1536-dimension models using this Elasticsearch Vector setup?

Yes, your agent uses the createindex tool to define the exact dimensions required by models like OpenAI's text-embedding-3-small. You simply specify the dimension count in your chat prompt, and the server configures the mapping.

Q: How do I verify which indexes are available?

Ask your agent to run the listindexes tool. It returns a clean list of all active indexes on your cluster, showing you exactly where you can run kNN vector queries.

Run dense vector kNN searches and manage Elasticsearch indexes directly from Claude Desktop without writing search queries by hand.

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Connect Elasticsearch Vector MCP to Claude Desktop

Create your Vinkius account to connect Elasticsearch Vector to Claude Desktop 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 Claude Desktop

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Run kNN searches inside Claude Desktop

The `search` tool lets your agent run dense vector kNN queries against your Elasticsearch cluster to pull relevant documents. Instead of writing complex query DSL by hand, you ask Claude Desktop to find similar items based on vector embeddings. This setup pulls raw vectors from your active index and presents the top matches directly in your chat window. Your agent parses the scoring metrics immediately, allowing you to debug retrieval accuracy during active conversations.

Build Elasticsearch Vector indexes in Claude Desktop

The `create_index` tool sets up dense vector mappings on your cluster without requiring Kibana or curl commands. You tell this MCP Server the dimension size, and it configures the HNSW algorithm settings on the fly. Running `get_index` verifies the mapping parameters directly inside your workspace. You instantly see if the cluster is ready to accept high-dimensional embeddings before starting raw document ingestion.

Index and delete documents on the fly

The `index_document` tool writes raw vectors and metadata payloads directly to your specified index. If a vector representation changes, your agent updates the record without leaving Claude Desktop. Cleaning up stale vector records takes a single command via the `delete_document` tool. Your agent removes outdated embeddings, which prevents your HNSW graphs from bloating and slowing down retrieval speed.

Setup guide

Set up Elasticsearch Vector MCP in Claude Web or Desktop

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.
3

Start a conversation

Open a new chat. The Elasticsearch Vector MCP tools are available immediately — no restart needed.

Endpoint URL

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

No configuration file needed — paste the URL directly in the Claude web interface.

Available on Free (1 connector), Pro, Max, Team, and Enterprise plans.

Prerequisites

Claude Desktop installed (macOS or Windows)
Active Vinkius subscription with a valid endpoint token

1

Open Claude Desktop Settings

Click the menu icon at the top-left corner, go to Settings → Developer → Edit Config. This opens claude_desktop_config.json in your default text editor.
2

Paste the Elasticsearch Vector MCP configuration

Copy the JSON snippet on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Desktop

Close and reopen the application. Claude needs a full restart to load new MCPs — refreshing a conversation is not enough.
4

Verify the connection

Open a new conversation. Click the 🔌 icon at the bottom of the message input. You should see tools listed under elasticsearch-vector-mcp.

json

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

Get your connection token →

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 Claude Desktop

Open your `claude_desktop_config.json` file and add the server to your `mcpServers` list. Provide your cluster endpoint and credentials as environment variables. Once you restart the app, the hammer icon will display the six vector search tools.

Yes, your agent uses the `create_index` tool to define the exact dimensions required by models like OpenAI's text-embedding-3-small. You simply specify the dimension count in your chat prompt, and the server configures the mapping.

Yes, you can connect this MCP server to claude.ai by adding it as a custom connector. You paste the remote HTTPS URL of your hosted server in your integration settings, and the tools become available in your browser window.

Ask your agent to run the `list_indexes` tool. It returns a clean list of all active indexes on your cluster, showing you exactly where you can run kNN vector queries.

Your vector embeddings and Elasticsearch credentials stay local to your machine or your secure remote endpoint. Vinkius runs the MCP server in an isolated sandbox, meaning your raw vector values are never cached, stored, or read by third parties.

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