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Connect Elasticsearch Vector to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Control your vector search and embeddings through natural language.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 6 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Elasticsearch Vector.

  1. 01

    Create index

    This capability sets up a new index specifically for dense_vector storage.

  2. 02

    Search

    Use this to run dense vector kNN searches against your existing indices.

  3. 03

    Get index

    This capability retrieves detailed information and metadata about a specific index.

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through Elasticsearch Vector.

  1. 04

    Index document

    This capability pushes a single document into your index.

  2. 05

    List indexes

    Use this to see all the indices currently active in your cluster.

  3. 06

    Delete document

    This capability removes a specific document from an index using its UUID.

Real work

What teams actually run

01

Embedding Model Testing

Quickly verify how different embedding models perform by running kNN searches against existing data.

02

Rapid Prototyping

Spin up new dense_vector indices and start indexing documents in seconds.

03

Data Integrity Audits

Check dimensional constraints and similarity thresholds to ensure search accuracy.

04

Real-time Index Management

Manage storage namespaces and document deletions through a chat interface.

Automate a task

Put Elasticsearch Vector to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Elasticsearch Vector works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Elasticsearch Vector to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

917ms average. Fast in production.

Elasticsearch Vector is checked daily against the live service.

Daily averagePeak 974ms
Oct 14Today
Fastest day
751ms
Slowest day
974ms
14-day trend
Slowing+14%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Elasticsearch Vector, so you can see the experience inside your AI.

It does not authenticate your account with Elasticsearch Vector. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Elasticsearch Vector Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_kUWPWShL0BRuDE0k7qrS1ZYbK8Cj21vwQZnEsEiQ/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Elasticsearch Vector capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "elasticsearch-vector-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_kUWPWShL0BRuDE0k7qrS1ZYbK8Cj21vwQZnEsEiQ/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Elasticsearch Vector in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Elasticsearch Vector owners hand off.

This MCP is built for technical teams working with high-dimensional data and semantic search.

  • 01

    AI Engineers

    They use the agent to test embedding models and run semantic searches without writing DSL.

  • 02

    Software Developers

    They index documents and verify kNN results directly from their IDE.

  • 03

    Data Scientists

    They monitor index mappings and check dimensional constraints via natural language.

  • 04

    Ops Teams

    They manage vector storage namespaces and check cluster health in real time.

FAQ

Questions Elasticsearch Vector owners ask.

  • 01

    How do I connect my Elasticsearch cluster?

    You need your Elasticsearch Host URL and an API Key. You can find the API Key in Kibana under Stack Management, then Security, then API Keys.

  • 02

    Which AI clients can use this MCP?

    This MCP works with any compatible client, including Claude, Cursor, Windsurf, and VS Code.

  • 03

    Can I create new indices through my agent?

    Yes, you can use the create_index capability to provision new dense_vector indices with specific dimensions.

  • 04

    Does this support kNN searches?

    Yes, the search capability is specifically designed to perform dense vector kNN computations.

  • 05

    How do I delete data?

    You can use the delete_document capability to remove specific records from your indices using their UUID.

  • 06

    Can my agent perform kNN searches using raw vector arrays?

    Yes. Use the 'search' capability. Provide the index name and a JSON array representing your query vector. The agent will perform raw K-Nearest Neighbors computations to find the most semantically similar documents.

  • 07

    How do I create a new vector index with specific dimensions via chat?

    Use the 'create_index' capability. You can specify the index name and the number of dimensions (e.g., 1536 for OpenAI embeddings). The agent will provision the strictly typed data structure in your Elasticsearch cluster.

  • 08

    Can I delete a single document from a vector index through the agent?

    Absolutely. Use the 'delete_document' capability with the index and document ID. The agent will enforce immediate document vaporization, stripping the record from the physical Lucene partitions.

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