Does Marqo handle the vector embeddings for me through the agent?

Yes. Marqo is an end-to-end engine. When you use the `tensor_search` tool, you provide natural language and Marqo handles the model inference and vector extraction under the hood, returning semantically relevant results immediately.

Can I add new data to a vector index through a conversation?

Absolutely. Use the `add_documents` tool by providing a JSON array of your documents. Your agent will synchronize these records into the target index, and they will be searchable via semantic query instantly.

How do I check the stats of my vector index?

The `get_index_stats` tool retrieves critical metrics for a specific index. Your agent will report the document count, memory usage, and configuration details, helping you monitor the health of your vector store.

Marqo AI (Vector Search & Embeddings) MCP Server for Google ADK 6 tools — connect in under 2 minutes

Name: Marqo AI (Vector Search & Embeddings)
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 Marqo AI (Vector Search & Embeddings) as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

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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="marqo_ai_vector_search_embeddings_agent",
    instruction=(
        "You help users interact with Marqo AI (Vector Search & Embeddings) "
        "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 Marqo AI (Vector Search & Embeddings) MCP Server

Connect your Marqo instance to any AI agent and take full control of your semantic search infrastructure, vector embeddings, and real-time document indexing through natural conversation.

Google ADK natively supports Marqo AI (Vector Search & Embeddings) 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

Tensor Search Orchestration — Execute dense semantic similarity searches against your indices using natural language queries, with Marqo handling embedding extraction automatically
Dynamic Document Ingestion — Write new JSON records into your vector indices directly from your agent, allowing for instant searchability of fresh data mappings
Index Lifecycle Management — Create explicitly bounded new vector indices with custom model settings and dimension constraints to optimize your search architecture
Vector Audit & Stats — Retrieve detailed configuration metrics for your indices, including document counts, embedding model types, and underlying schema mappings
Precision Deletion — Physically eradicate vectorized representations by targeting specific scalar identifiers to maintain a clean and relevant search index
Resource Inventory — List all available vector indices on your Marqo instance to identify collection boundaries before executing search queries

The Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) to Google ADK via MCP

Follow these steps to integrate the Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) via MCP

Why Use Google ADK with the Marqo AI (Vector Search & Embeddings) MCP Server

Google ADK provides unique advantages when paired with Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings)

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 Marqo AI (Vector Search & Embeddings) tools with BigQuery, Vertex AI, and Cloud Functions

Marqo AI (Vector Search & Embeddings) + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Marqo AI (Vector Search & Embeddings) MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Marqo AI (Vector Search & Embeddings) and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Marqo AI (Vector Search & Embeddings) tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Marqo AI (Vector Search & Embeddings) regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Marqo AI (Vector Search & Embeddings)

Marqo AI (Vector Search & Embeddings) MCP Tools for Google ADK (6)

These 6 tools become available when you connect Marqo AI (Vector Search & Embeddings) to Google ADK via MCP:

add_documents

Write new documents into Marqo

create_index

Create an explicitly bounded new vector index

delete_documents

Delete specific documents from Marqo by targeting their IDs

get_index_stats

Get configuration and stats for an index

list_indexes

Crucial before writing queries hitting arbitrary collections. List all Marqo vector indexes

tensor_search

Perform natural language tensor search on Marqo

Example Prompts for Marqo AI (Vector Search & Embeddings) in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Marqo AI (Vector Search & Embeddings) immediately.

"Semantic search in index 'products' for 'lightweight running shoes for trails'"

"List all vector indexes in my Marqo instance"

"Add this document to the 'support-docs' index: {"title": "API Auth", "content": "Use Marqo-API-Key header"}"

Troubleshooting Marqo AI (Vector Search & Embeddings) MCP Server with Google ADK

Common issues when connecting Marqo AI (Vector Search & Embeddings) to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Marqo AI (Vector Search & Embeddings) + Google ADK FAQ

Common questions about integrating Marqo AI (Vector Search & Embeddings) 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.