How to Use the Weaviate MCP in Google ADK

Q: How do I use Weaviate and the Google ADK to find related documents?

You execute searchnearvector. Your agent supplies a class name and the query vector. Weaviate handles the comparison, giving your Gemini-powered agent precise context.

Q: Can the Google ADK read object details from Weaviate?

Yes. The getobjectdetails tool retrieves all metadata for a specific data object using its UUID. This is critical when connecting cloud-based agents to structured records.

Q: Does the Google ADK handle pagination when listing Weaviate objects?

Yes, listobjects supports basic pagination with a limit parameter. This means your agent can reliably loop through large sets of data within a class.

Q: Which tool do I use in the Google ADK to understand Weaviate's overall structure?

Start with getfullschema if you need all collections, or narrow it down using getclassschema for a single class definition.

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Connect Weaviate MCP to Google ADK

Create your Vinkius account to connect Weaviate 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 Weaviate with Google ADK

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Perplexity

Vector Similarity Search

The `search_near_vector` tool lets your agent query complex, unstructured data. Just pass a class name and the vector array; Weaviate does the heavy lifting to find nearest neighbors. This is how you ground long-context reasoning in Google ADK. Instead of guessing, your agent retrieves specific context from Weaviate.

Instance Operational Details

The `get_cluster_nodes` tool reports on the operational status of the entire Weaviate cluster. Your agent can use this to check if all nodes are available before starting a complex task. It also provides basic system health checks via `get_instance_metadata`, letting your enterprise workflow fail fast if infrastructure is down.

Detailed Schema Management

Need to check the data model? Use `get_full_schema` to get every class definition. Alternatively, `get_class_schema` provides definitions for a single collection. This lets your agent maintain an accurate understanding of the BigQuery source data structure when acting on Weaviate.

Setup guide

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

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Common questions about Weaviate MCP in Google ADK

You execute `search_near_vector`. Your agent supplies a class name and the query vector. Weaviate handles the comparison, giving your Gemini-powered agent precise context.

Yes. The `get_object_details` tool retrieves all metadata for a specific data object using its UUID. This is critical when connecting cloud-based agents to structured records.

Yes, `list_objects` supports basic pagination with a limit parameter. This means your agent can reliably loop through large sets of data within a class.

Start with `get_full_schema` if you need all collections, or narrow it down using `get_class_schema` for a single class definition.

This server touches object UUIDs and vector float arrays. These are the primary identifiers and context inputs used when calling `get_object_details` or `search_near_vector`.

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