How to Use the Redis Vector MCP in Google ADK

Q: How does Google ADK interact with Redis Vector for similarity queries?

You instantiate the McpToolset and pass it to your agent. When the model needs data, it calls searchvectors to query your Redis Vector store.

Q: Can I use Redis Vector to store data processed by Google ADK?

Yes. You can parse your data into float arrays and use upsertvector to store the resulting embeddings in Redis.

Connect Google ADK to your Redis Vector store for high-performance vector search in your enterprise cloud agents.

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Connect Redis Vector MCP to Google ADK

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Setup Redis Vector with Google ADK

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Google ADK vector index administration

Manage your Redis indexes through `create_vector_index` to align your vector space with your existing cloud data. It maps your high-dimensional embeddings directly to your Redis setup. Run `get_index_info` to monitor your index health. This gives your agent visibility into the structure of your data before it attempts a search.

Efficient vector retrieval for Gemini models

The `search_vectors` tool allows your Gemini-powered agent to perform KNN searches against your Redis Vector store. It processes your input as a float array, returning vectors that match your query requirements. This integration lets you use your existing Redis infrastructure with Google's reasoning capabilities. You avoid the overhead of custom middleware.

Real-time vector persistence for cloud agents

Execute `upsert_vector` to update your Redis hashes as new data arrives. It keeps your agent's long-term memory synchronized with your latest information. `delete_vector` allows for fine-grained control over your data lifecycle. Your agent maintains the index state without manual intervention.

Setup guide

Set up Redis Vector 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 Redis Vector 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="Redis Vector_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Redis Vector tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Redis Vector MCP in Google ADK

You instantiate the `McpToolset` and pass it to your agent. When the model needs data, it calls `search_vectors` to query your Redis Vector store.

Yes. You can parse your data into float arrays and use `upsert_vector` to store the resulting embeddings in Redis.

You can restrict the tools available to your agent by using the tool_names filter during initialization. This ensures your agent only touches the indexes you approve.

It does. You can configure the server using Stdio or HTTP transports depending on your specific cloud deployment requirements.

Your vector embeddings remain encrypted at rest and in transit within the Vinkius sandbox. Access is restricted to your specific endpoint token.

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