Vertex AI Vector Search 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 Vertex AI Vector Search as an MCP tool provider through the 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="vertex_ai_vector_search_agent",
instruction=(
"You help users interact with Vertex AI Vector Search "
"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 Vertex AI Vector Search MCP Server
Plug the sheer matching scale of Google Cloud's Vertex AI Vector Search directly into your intelligent IDE or conversational agent. Unleash low-latency nearest neighbor lookups across billion-scale embedding structures without navigating Cloud Console interfaces.
Google ADK natively supports Vertex AI Vector Search 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
- Massive Semantic Extraction — Prompt your agent to formulate query vectors and blast them at your specialized Cloud endpoints. It instantly retrieves identical geometric text boundaries (nearest neighbors) to ground LLM contexts powerfully.
- Index Operations — Gain total situational awareness over your massive datasets. Command the bot to list your provisioned Vector Indexes, verifying dimensionality, configuration updates, and current active states within seconds.
- Endpoint Monitoring — List active network endpoints scaling your specific RAG applications. Determine clearly which underlying deployed index iterations are currently receiving production traffic without digging through IAM screens.
- Operation Tracking — Spun up a multi-terabyte index build? Query the cloud queue using chat to review persistent long-running task timelines from your primary editor.
The Vertex AI Vector Search 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 Vertex AI Vector Search to Google ADK via MCP
Follow these steps to integrate the Vertex AI Vector Search 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 Vertex AI Vector Search via MCP
Why Use Google ADK with the Vertex AI Vector Search MCP Server
Google ADK provides unique advantages when paired with Vertex AI Vector Search 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 Vertex AI Vector Search
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 Vertex AI Vector Search tools with BigQuery, Vertex AI, and Cloud Functions
Vertex AI Vector Search + Google ADK Use Cases
Practical scenarios where Google ADK combined with the Vertex AI Vector Search MCP Server delivers measurable value.
Enterprise data agents: ADK agents query Vertex AI Vector Search and cross-reference results with internal databases for comprehensive analysis
Multi-modal workflows: combine Vertex AI Vector Search tool responses with Gemini's vision and language capabilities in a single agent
Automated compliance checks: schedule ADK agents to query Vertex AI Vector Search regularly and flag policy violations or configuration drift
Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Vertex AI Vector Search
Vertex AI Vector Search MCP Tools for Google ADK (6)
These 6 tools become available when you connect Vertex AI Vector Search to Google ADK via MCP:
get_index_details
Retrieves metadata and configuration for a specific vector index
list_deployed_indexes
Lists all indexes deployed to a specific endpoint
list_index_endpoints
Lists all index endpoints in the project
list_vector_indexes
Lists all vector indexes in the Google Cloud project
list_vector_operations
Lists long-running operations related to vector indexes
search_nearest_neighbors
Provide the endpoint ID, deployed index ID, and a query vector as a JSON array. Performs a nearest neighbor vector similarity search
Example Prompts for Vertex AI Vector Search in Google ADK
Ready-to-use prompts you can give your Google ADK agent to start working with Vertex AI Vector Search immediately.
"List all our active vector indexes on the current GCP project."
"Check for any long-running vector deployment operations currently uncompleted."
"Find the 3 nearest neighbors mapping to endpoint '39xl' array index ID 'dep_30' using vector [-0.2, 0.5, 0.0]."
Troubleshooting Vertex AI Vector Search MCP Server with Google ADK
Common issues when connecting Vertex AI Vector Search to Google ADK through the Vinkius, and how to resolve them.
McpToolset not found
pip install --upgrade google-adkVertex AI Vector Search + Google ADK FAQ
Common questions about integrating Vertex AI Vector Search 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?
Connect Vertex AI Vector Search with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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TypeScript toolkit for building AI-powered web applications.
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Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Vertex AI Vector Search to Google ADK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.