How to Use the Zilliz Cloud MCP in Google ADK

Q: How does Google ADK query data from Zilliz Cloud?

The agent invokes queryentities. You supply metadata filters that constrain the search space, ensuring your long-context reasoning focuses only on the required records.

Q: Can I manage multiple collections using Zilliz Cloud and Google ADK?

Yes. You start by listing them with listcollections. From there, you can create new ones via createcollection or remove old ones using dropcollection.

Q: If I run a search query, what tools are utilized when using Zilliz Cloud and Google ADK?

The core tool is searchvectors. The agent also uses queryentities if the initial request needs to be filtered by specific metadata fields before running the vector comparison.

Act on BigQuery data using the Google ADK and Zilliz Cloud for enterprise agents.

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Connect Zilliz Cloud MCP to Google ADK

Create your Vinkius account to connect Zilliz Cloud to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Zilliz Cloud with Google ADK

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Vector Search & Retrieval

The agent uses `search_vectors` to run similarity searches against your vectors. It accepts a JSON search configuration, guaranteeing the right inputs are sent to Zilliz Cloud. This lets you connect BigQuery data with vector context. Your agent can now perform sophisticated lookups using metadata filtering alongside proximity matching.

Managing Collections

Need visibility? Call `list_collections` to see every collection in the cluster. If you need specifics, use `describe_collection` on a named group. You can create new storage areas with `create_collection`, or clean up old ones using `drop_collection`.

Data Ingestion

To bring data into the system, use `insert_entities`. It takes structured input and adds it to your collection. Alternatively, if you want all the raw records available instantly, run `load_collection`.

Setup guide

Set up Zilliz Cloud MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Zilliz Cloud tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Zilliz Cloud_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Zilliz Cloud tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Zilliz Cloud MCP in Google ADK

The agent invokes `query_entities`. You supply metadata filters that constrain the search space, ensuring your long-context reasoning focuses only on the required records.

This server touches vector and entity data. Because you're running enterprise agents, all operations through the Google ADK are designed to maintain strict control over how your client interacts with Zilliz Cloud.

Yes. You start by listing them with `list_collections`. From there, you can create new ones via `create_collection` or remove old ones using `drop_collection`.

Absolutely. If the agent needs to work with an entire dataset repeatedly, `load_collection` pulls all entities into memory, which speeds up downstream processing significantly.

The core tool is `search_vectors`. The agent also uses `query_entities` if the initial request needs to be filtered by specific metadata fields before running the vector comparison.

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