How to Use the Landbot MCP in Google ADK

Q: How do I add Landbot tools to my Google ADK agent?

After installing the ADK, create an McpToolset instance. You'll point it to your Vinkius endpoint using StreamableHttpServerParameters. Then, just pass the toolset to your LlmAgent constructor.

Q: Can a Gemini agent on Google ADK access my Landbot customer list?

Yes, the listlandbotcustomers tool exposes that. Your agent can fetch the list and use it to feed other processes, like sending a targeted campaign with sendproactiveimage.

Q: How can I restrict which Landbot tools my Google ADK agent can use?

When you create the McpToolset, you can use the toolnames filter. This lets you provide an explicit list of tools, like only allowing getcustomerdetails and triggerbotflow, to enforce security boundaries for your Gemini agent.

Q: Is it possible to check Landbot system status with a Google ADK agent?

The getaccountinfo tool acts as a simple health check. Your agent can call it to confirm the API is responsive before attempting more complex operations.

Connect Gemini to Landbot using the Google ADK for enterprise-scale customer automation on Google Cloud.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Landbot MCP to Google ADK

Create your Vinkius account to connect Landbot 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.

GDPR Free for Subscribers

Setup Landbot with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Connect Landbot to your Google Cloud Data

The Google ADK is designed to work with your existing GCP data. Your Gemini agent can pull customer segments from a BigQuery table, then use this MCP Server to iterate through them and `send_proactive_text` or a `send_whatsapp_template` to each one. Imagine running a churn-prediction model in Vertex AI. The ADK lets your agent take the output list of at-risk users, find their details with `get_customer_details` in Landbot, and then `trigger_bot_flow` to start a retention campaign automatically.

Scale Customer Interactions with Gemini

Use the long-context capabilities of Gemini to manage complex customer service scenarios. An agent can pull a user's entire chat history, analyze it, and then decide the best course of action—whether it's updating their profile with `update_customer_field` or escalating to a human with `handoff_to_agent`. Your agent can first check for available staff with `list_team_agents` to make an informed routing decision. The Google ADK allows the agent to hold all this context in memory, so it doesn't need to repeatedly query for the same information, making it more efficient at scale.

Build Enterprise-Grade Bot Orchestration

This isn't just for one-off tasks. Your Google ADK agent can function as a master controller for your entire Landbot setup. It can get a high-level view by calling `list_active_bots` and `list_landbot_customers` to understand the current state of your operations. From there, it can make strategic decisions. For example, if it detects a spike in failed conversations, it could analyze the webhook logs retrieved via `list_message_hooks` and alert an operations team, all without human intervention.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Landbot. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Landbot now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Landbot MCP in Google ADK

After installing the ADK, create an `McpToolset` instance. You'll point it to your Vinkius endpoint using `StreamableHttpServerParameters`. Then, just pass the toolset to your `LlmAgent` constructor.

Yes, the `list_landbot_customers` tool exposes that. Your agent can fetch the list and use it to feed other processes, like sending a targeted campaign with `send_proactive_image`.

When you create the `McpToolset`, you can use the `tool_names` filter. This lets you provide an explicit list of tools, like only allowing `get_customer_details` and `trigger_bot_flow`, to enforce security boundaries for your Gemini agent.

The `get_account_info` tool acts as a simple health check. Your agent can call it to confirm the API is responsive before attempting more complex operations.

The MCP Server acts as a stateless proxy. When your agent requests customer information via `get_customer_details`, the server passes the request to Landbot and streams the response back through a secure Vinkius channel. No customer records are ever persisted on the server.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript