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 Google ADK 6 tools — connect in under 2 minutes

Name: Elasticsearch Vector
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 6 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Elasticsearch Vector as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="elasticsearch_vector_agent",
    instruction=(
        "You help users interact with Elasticsearch Vector "
        "using 6 available tools."
    ),
    tools=[mcp_tools],
)

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* 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.

Google ADK natively supports Elasticsearch Vector as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 6 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

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 Google ADK 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 Google ADK via MCP

Follow these steps to integrate the Elasticsearch Vector MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 6 tools from Elasticsearch Vector via MCP

Why Use Google ADK with the Elasticsearch Vector MCP Server

Google ADK provides unique advantages when paired with Elasticsearch Vector through the Model Context Protocol.

Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Elasticsearch Vector

Production-ready features like session management, evaluation, and deployment come built-in — not bolted on

Seamless integration with Google Cloud services means you can combine Elasticsearch Vector tools with BigQuery, Vertex AI, and Cloud Functions

Elasticsearch Vector + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Elasticsearch Vector MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Elasticsearch Vector and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Elasticsearch Vector tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Elasticsearch Vector regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Elasticsearch Vector

Elasticsearch Vector MCP Tools for Google ADK (6)

These 6 tools become available when you connect Elasticsearch Vector to Google ADK 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 Google ADK

Ready-to-use prompts you can give your Google ADK 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 Google ADK

Common issues when connecting Elasticsearch Vector to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Elasticsearch Vector + Google ADK FAQ

Common questions about integrating Elasticsearch Vector MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.