Can my agent perform kNN searches using raw vector arrays?

Yes. Use the 'search' tool. 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.

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

Use the 'create_index' tool. 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.

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

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

Elasticsearch Vector MCP Server for VS Code Copilot 6 tools — connect in under 2 minutes

Name: Elasticsearch Vector
Availability: InStock
Author: Vinkius

code.visualstudio.com/docs/copilot/chat/mcp-servers

Built by Vinkius GDPR 6 Tools IDE

GitHub Copilot in VS Code is the most widely adopted AI coding assistant, embedded directly into the world's most popular code editor. With MCP support in Agent mode, Copilot can access external data and APIs to generate context-aware code grounded in real-time information.

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The modern way to manage MCP Servers — no config files, no terminal commands. Install Elasticsearch Vector and 2,500+ MCP Servers from a single visual interface.

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Classic Setup·json

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

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Elasticsearch Vector MCP Server

Connect your Elasticsearch cluster to any AI agent and take full control of your vector search and semantic discovery workflows through natural conversation.

GitHub Copilot Agent mode brings Elasticsearch Vector data directly into your VS Code workflow. With a project-scoped config, the entire team shares access to 6 tools. Copilot queries live data, generates typed code, and writes tests from actual API responses, all without leaving the editor.

What you can do

AI-Powered Vector Search — Perform raw K-Nearest Neighbors (kNN) computations mapping absolute semantic similarity across multi-dimensional embedding arrays
Index Orchestration — Enumerate active storage namespaces and validate physical Elasticsearch clusters tracking explicit dimensional shards securely
Schema Management — Analyze specific index mapping rules and provision strictly typed data structures enforcing numeric dimensions for cluster readiness
Document Indexing — Command synchronous bulk insertions attaching exact dense_vector embedding payloads to persist data into raw Lucene partitions
Data Invalidation — Enforce immediate hard document vaporization finding specific exact UUIDs stripping records from physical indices seamlessly
Metadata Auditing — Analyze dimensional constraints and matching similarity thresholds perfectly to verify your vector search configurations

The Elasticsearch Vector MCP Server exposes 6 tools through the Vinkius. Connect it to VS Code Copilot in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Elasticsearch Vector to VS Code Copilot via MCP

Follow these steps to integrate the Elasticsearch Vector MCP Server with VS Code Copilot.

Create MCP config

Create a .vscode/mcp.json file in your project root

Add the server config

Paste the JSON configuration above

Enable Agent mode

Open GitHub Copilot Chat and switch to Agent mode using the dropdown

Start using Elasticsearch Vector

Ask Copilot: "Using Elasticsearch Vector, help me...". 6 tools available

Why Use VS Code Copilot with the Elasticsearch Vector MCP Server

GitHub Copilot for Visual Studio Code provides unique advantages when paired with Elasticsearch Vector through the Model Context Protocol.

VS Code is used by over 70% of developers. adding MCP tools to Copilot means your team can leverage external data without leaving their primary editor

Project-scoped MCP configs (`.vscode/mcp.json`) let you commit server configurations to your repository, ensuring the entire team shares the same tool access

Copilot's Agent mode integrates MCP tools seamlessly with file editing, terminal commands, and workspace search in a single agentic loop

GitHub's enterprise compliance and audit features extend to MCP tool usage, providing visibility into how AI interacts with external services

Elasticsearch Vector + VS Code Copilot Use Cases

Practical scenarios where VS Code Copilot combined with the Elasticsearch Vector MCP Server delivers measurable value.

Live API integration: Copilot can query an MCP server, inspect the response schema, and generate typed API client code in the same step

DevSecOps workflows: security teams can give developers access to domain intelligence tools directly in their editor for real-time vulnerability assessment during code review

Data pipeline development: Copilot fetches sample data via MCP and generates transformation scripts, validators, and test fixtures from actual API responses

Documentation generation: Copilot queries available tools and auto-generates README sections, API reference docs, and usage examples

Elasticsearch Vector MCP Tools for VS Code Copilot (6)

These 6 tools become available when you connect Elasticsearch Vector to VS Code Copilot via MCP:

create_index

Create dense_vector index

delete_document

Delete a document

get_index

Get index info

index_document

Index a document

list_indexes

List all indexes

search

Dense vector knn search

Example Prompts for Elasticsearch Vector in VS Code Copilot

Ready-to-use prompts you can give your VS Code Copilot agent to start working with Elasticsearch Vector immediately.

"Perform a kNN search in index 'product-embeddings' with vector [0.1, 0.2, ...]"

"Create a new vector index 'image-features' with 512 dimensions"

"List all vector indexes in my cluster"

Troubleshooting Elasticsearch Vector MCP Server with VS Code Copilot

Common issues when connecting Elasticsearch Vector to VS Code Copilot through the Vinkius, and how to resolve them.

MCP tools not available

Ensure you are in Agent mode in Copilot Chat. MCP tools only appear in Agent mode.

Elasticsearch Vector + VS Code Copilot FAQ

Common questions about integrating Elasticsearch Vector MCP Server with VS Code Copilot.

Which VS Code version supports MCP?

MCP support requires VS Code 1.99 or later with the GitHub Copilot extension. Ensure both are updated to the latest version. Older versions of Copilot may not expose the Agent mode toggle.

How do I switch to Agent mode?

Open the Copilot Chat panel and look for two mode options: "Ask" and "Agent". Click "Agent" to enable autonomous tool calling. In Ask mode, Copilot provides conversational answers but cannot invoke MCP tools.

Can I restrict which MCP tools Copilot can access?

Yes. VS Code shows a tool consent dialog before any MCP tool is invoked for the first time. You can also configure tool access policies at the organization level through GitHub Copilot settings.

Does MCP work in VS Code Remote or Codespaces?

Yes. MCP servers configured via .vscode/mcp.json work in Remote SSH, WSL, and GitHub Codespaces environments. The MCP connection is established from the remote host, so ensure the server URL is accessible from that environment.