How to Use the Verba MCP in Google ADK

Q: What is the best way to check document status using Verba with Google ADK?

Use listknowledgedocuments to get a count of all indexed items. If you need deep details on one file, call getdocumentdetails. This keeps your agent running reliably.

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Connect Verba MCP to Google ADK

Create your Vinkius account to connect Verba 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 Verba with Google ADK

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Perplexity

Execute deep research using the MCP Server.

Need to search through massive corpuses? Use `perform_rag_query` for Retrieval Augmented Generation. The tool reads your documents, pulls out key information, and gives you summarized answers with citations. This works great when pairing Verba RAG with BigQuery data sources. Your agent can act on complex knowledge retrieval, letting Gemini hold context across 1M+ tokens.

Maintain the enterprise knowledge graph.

You control what gets indexed. When a new document is ready, call `add_knowledge_document` to ingest it into Verba's knowledge base, including necessary metadata JSON. If content goes stale, you use `delete_knowledge_document` to wipe it out completely. This toolset gives the agent full control over its source of truth.

Retrieve and inspect system data.

The agent needs visibility. You can run `list_knowledge_documents` to see every document indexed in Verba. For specifics, use `get_document_details` to grab the full content of a single file. Always check `get_system_config` first if you're unsure about the operational parameters. These inspection tools let your agent verify its operating environment before taking action.

Setup guide

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

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Common questions about Verba MCP in Google ADK

You pass the MCP Toolset object to your LlmAgent. This native integration lets your Gemini-powered agent treat Verba as a core, reliable data source for enterprise operations.

Absolutely. The MCP Server allows your agent to pull semantic answers from Verba's documents and combine that knowledge with the structured data you already keep in BigQuery.

Use `list_knowledge_documents` to get a count of all indexed items. If you need deep details on one file, call `get_document_details`. This keeps your agent running reliably.

Yes. The MCP Server provides a controlled layer of access, ensuring that your Gemini models only retrieve verified answers through tools like `perform_rag_query`.

The server manages document content and metadata JSON. This knowledge base is what your agent queries when it needs context that isn't already loaded into its massive token window.

Use it with your favorite AI tools

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