OpenSearch Vector MCP Server for Google ADK 6 tools — connect in under 2 minutes
Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add OpenSearch Vector as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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="opensearch_vector_agent",
instruction=(
"You help users interact with OpenSearch Vector "
"using 6 available tools."
),
tools=[mcp_tools],
)
* 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 OpenSearch Vector MCP Server
Turn your OpenSearch cluster into an AI-native vector database. Create k-NN indexes, upsert embeddings, run similarity searches, and inspect index configurations — all through natural conversation with your AI agent.
Google ADK natively supports OpenSearch Vector as an MCP tool provider. declare 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
- Vector Search — Execute k-Nearest Neighbors queries against any k-NN index with custom top-K limits and dense float vectors
- Index Management — List all cluster indexes with health status and document counts, or inspect a specific index's vector dimension, engine config, and distance metric
- Create Index — Provision new k-NN indexes optimized for cosine similarity with configurable vector dimensions (384, 768, 1536, etc.)
- Document Operations — Upsert vector documents with metadata, or delete documents from the embedding space by ID
The OpenSearch 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 OpenSearch Vector to Google ADK via MCP
Follow these steps to integrate the OpenSearch 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 OpenSearch Vector via MCP
Why Use Google ADK with the OpenSearch Vector MCP Server
Google ADK provides unique advantages when paired with OpenSearch 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 OpenSearch 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 OpenSearch Vector tools with BigQuery, Vertex AI, and Cloud Functions
OpenSearch Vector + Google ADK Use Cases
Practical scenarios where Google ADK combined with the OpenSearch Vector MCP Server delivers measurable value.
Enterprise data agents: ADK agents query OpenSearch Vector and cross-reference results with internal databases for comprehensive analysis
Multi-modal workflows: combine OpenSearch Vector tool responses with Gemini's vision and language capabilities in a single agent
Automated compliance checks: schedule ADK agents to query OpenSearch 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 OpenSearch Vector
OpenSearch Vector MCP Tools for Google ADK (6)
These 6 tools become available when you connect OpenSearch Vector to Google ADK via MCP:
create_index
knn: true` and mapping a rigid dynamic dense vector field optimized for cosine similarity. Create a new native OpenSearch KNN index ready for vector embeddings
delete_document
Delete an explicit vector document bounding from OpenSearch
get_index
Retrieve explicit OpenSearch index mapping and settings
index_document
This executes a fast transactional atomic insertion into the embedding space. Upsert a singular vector document directly into an OpenSearch KNN index
list_indexes
List all explicit indexes residing on the OpenSearch cluster
search
Provide the exact index name and a JSON-stringified dense float vector array to find conceptually similar embeddings natively. Execute a K-Nearest Neighbors (k-NN) vector search against OpenSearch
Example Prompts for OpenSearch Vector in Google ADK
Ready-to-use prompts you can give your Google ADK agent to start working with OpenSearch Vector immediately.
"List all vector indexes in my OpenSearch cluster."
"Find the 5 most similar documents to this embedding in the knowledge-base index."
"Create a new k-NN index called 'customer-feedback' with 1536 dimensions."
Troubleshooting OpenSearch Vector MCP Server with Google ADK
Common issues when connecting OpenSearch Vector to Google ADK through the Vinkius, and how to resolve them.
McpToolset not found
pip install --upgrade google-adkOpenSearch Vector + Google ADK FAQ
Common questions about integrating OpenSearch Vector MCP Server with Google ADK.
How does Google ADK connect to MCP servers?
Can ADK agents use multiple MCP servers?
Which Gemini models work best with MCP tools?
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Connect OpenSearch Vector to Google ADK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.