How to Use the Ada MCP in Google ADK

Q: How does Gemini's long context benefit my Ada support analysis?

Gemini can hold over a million tokens, meaning you can run listconversations to pull massive amounts of support logs and feed them all into a single prompt for deep trend analysis.

Q: Can I export Ada end-user profiles to BigQuery using Google ADK?

Yes, your agent can fetch profiles using getenduser and write those custom metavariables directly into BigQuery tables using standard ADK cloud integrations.

Feed Ada customer support histories directly into your Google ADK agents for deep reasoning over millions of tokens.

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Connect Ada MCP to Google ADK

Create your Vinkius account to connect Ada 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 Ada with Google ADK

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Analyze Millions of Ada Support Logs via Google ADK

Load your entire customer support history into Gemini's massive context window. By calling `list_conversations` over this MCP connection, your Google ADK agent pulls active and historical support threads to identify recurring product complaints or system outages in real time. This integration lets you process thousands of chats at once. Instead of reading individual tickets, your agent analyzes the whole batch to find patterns and suggests updates to your knowledge base.

Sync Ada Support Articles with BigQuery Data

Keep your help docs aligned with your warehouse records. Your Google ADK agent can call `list_articles` to pull your current help catalog, cross-reference it with product updates in BigQuery, and use `create_article` to publish missing documentation. This workflow closes the gap between product releases and support readiness. Cloud-hosted models handle the heavy lifting of drafting and updating help content, running entirely within your Google Cloud ecosystem.

Map Enterprise User Profiles with Google ADK MCP Server

Match real-time support requests with your cloud database records. The agent runs `get_end_user` to pull custom metavariables from Ada, then matches that data against enterprise customer profiles stored in Vertex AI. This lookup gives your Gemini models the exact context needed to personalize support responses. Your ADK agent makes decisions based on actual subscription tiers and historical user behavior without manual database queries.

Setup guide

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

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Common questions about Ada MCP in Google ADK

Yes, you can use the tool names filter in the Google ADK setup. This lets you restrict the agent to read-only tools like `list_articles` while blocking destructive or writing tools like `create_article`.

Gemini can hold over a million tokens, meaning you can run `list_conversations` to pull massive amounts of support logs and feed them all into a single prompt for deep trend analysis.

No, the Google ADK connects directly to the Vinkius-hosted HTTP endpoint. You pass the MCP server parameters to the toolset, and your cloud-hosted agents get instant access to your Ada data.

Yes, your agent can fetch profiles using `get_end_user` and write those custom metavariables directly into BigQuery tables using standard ADK cloud integrations.

Your support articles and user profiles are transmitted over encrypted TLS connections managed by Vinkius. The MCP Server acts as an ephemeral translator, ensuring your internal help documentation never leaks to public training sets.

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