How to Use the Chanty MCP in Google ADK

Q: How do I configure the Chanty MCP Server with the Google ADK?

Use McpToolset with your Vinkius HTTP endpoint inside your Python script. Pass the resulting toolset object into your LlmAgent configuration to expose the tools.

Deploy enterprise-grade Chanty automation workflows using Google ADK and Gemini.

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Connect Chanty MCP to Google ADK

Create your Vinkius account to connect Chanty 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 Chanty with Google ADK

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Enterprise Chanty management via Google ADK

The `list_messages` tool connects Gemini's million-token context window to your team chats. Your agent parses massive chat histories and extracts key action items without hitting context limits. Running this MCP Server gives your Google Cloud agents direct access to active chat rooms. It bridges the gap between your workspace conversations and your enterprise data pipelines.

Keep team status and profiles updated

Updating presence using the `set_status` tool syncs your HR databases with your team's chat presence. The agent updates user availability and checks permissions with `get_profile` to make sure changes are valid. This guarantees that your team's chat statuses match their actual working hours. You configure the agent once, and it handles the background updates quietly.

Programmatic conversation layout discovery

Querying active layouts with the `list_conversations` tool maps out your team's communication structure. Your agent analyzes these patterns to find inactive channels and flags them for archive. This tool gives your Gemini-based agent a clear view of your workspace. It handles organization tasks that usually take hours of manual checking.

Setup guide

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

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Why Choose Vinkius

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

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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 Chanty MCP in Google ADK

Use `McpToolset` with your Vinkius HTTP endpoint inside your Python script. Pass the resulting toolset object into your `LlmAgent` configuration to expose the tools.

Yes, you can use the optional tool name filter in the ADK setup. This lets you expose only specific tools like `send_message` while keeping administrative tools hidden.

Yes, the framework handles both transport layers. For hosted Vinkius MCP Server setups, you will use the streamable HTTP transport setup.

Absolutely. Your agent reads the BigQuery dataset, formats the summary, and executes `send_message` to share the report with your team.

We isolate every request inside a zero-trust execution environment. Your profile data and API keys never touch persistent disks and are discarded the moment the tool execution finishes.

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