How to Use the AB.GL MCP in Google ADK

Q: Can I limit which AB.GL tools my Google ADK agent can execute?

Yes, you can pass a toolnames filter to the toolset. This restricts the agent to reading statistics via getlinkstats while blocking destructive actions like deletelink.

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Connect AB.GL MCP to Google ADK

Create your Vinkius account to connect AB.GL 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 AB.GL with Google ADK

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BigQuery data enrichment using this MCP Server

Your Gemini agent uses `get_link_stats` to fetch real-time engagement data and writes it directly to BigQuery. Auditing your entire URL inventory is handled by `list_links` to clean up dead links.

Automated link generation inside Google ADK

Generate tracking links using the `create_short_link` tool during long-context reasoning tasks. If destination targets change, the agent executes `update_link` to keep campaigns active.

Enterprise brand and bio page control

Manage your corporate web presence directly from your cloud console with the `list_domains` tool. By running `list_bio_links` and `get_bio_link`, your pipeline updates bio profiles based on current cloud data.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AB.GL. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about AB.GL MCP in Google ADK

Use the McpToolset class with your HTTP server parameters. Pass this toolset into the LlmAgent constructor to let Gemini auto-discover tools like `create_short_link`.

Yes, you can pass a tool_names filter to the toolset. This restricts the agent to reading statistics via `get_link_stats` while blocking destructive actions like `delete_link`.

Gemini models process your vast document libraries and extract long destination URLs. The agent then feeds those clean strings directly into `create_short_link` without truncating the data.

You can run this setup using both Stdio and HTTP transports. This gives you the flexibility to host the MCP server locally during development or deploy it to Google Cloud Run.

Your redirect destinations and domain lists are processed inside an ephemeral V8 sandbox. This architecture guarantees that no sensitive URL strings or tracking codes leak into Google's public training datasets.

Use it with your favorite AI tools

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