How to Use the Freshchat MCP in Google ADK

Q: Can I filter tools when using Freshchat with Google ADK?

Yes. You can use the toolnames filter in your McpToolset definition. This restricts the agent to only the specific functions you want to expose.

Q: Does Google ADK support large context windows with this MCP Server?

Gemini models process the chat data retrieved from listchatmessages using their native 1M+ token capacity. It handles massive histories without losing coherence.

Integrate Freshchat with your Google ADK agents to handle enterprise support queries at scale.

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Connect Freshchat MCP to Google ADK

Create your Vinkius account to connect Freshchat 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 Freshchat with Google ADK

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Enterprise-grade conversation tracking

Your agent monitors high-volume queues by calling `list_conversations`. It feeds this data into your Gemini-powered pipelines for long-context analysis. Everything is native to your Google ADK setup. You can correlate support chat history with your existing BigQuery data to build a complete view of the user journey.

Targeted support agent retrieval

The agent identifies the best support staff using `list_support_agents`. It filters through `list_agent_groups` to find the team currently handling specific technical issues. This ensures your Google ADK agents route queries to the correct human experts. It eliminates the guesswork and latency involved in manual ticket assignment.

Dynamic messaging and user search

Locate specific users with `search_chat_users` to pull their historical records. Your agent executes `send_chat_message` to resolve common issues automatically. It works within your existing Google Cloud infrastructure. The integration keeps your support operations tightly coupled with your broader data strategy.

Setup guide

Set up Freshchat 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 Freshchat 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="Freshchat_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Freshchat 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 Freshchat. 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 Freshchat MCP in Google ADK

You use the McpToolset class to wrap the server parameters. It maps the tools directly to your LLMAgent, allowing for seamless execution.

Yes. You can use the tool_names filter in your McpToolset definition. This restricts the agent to only the specific functions you want to expose.

It supports both Stdio and HTTP transports. You can run the server locally for development before moving to your Cloud environment.

Gemini models process the chat data retrieved from `list_chat_messages` using their native 1M+ token capacity. It handles massive histories without losing coherence.

The server transmits data over encrypted channels. It only interacts with user contact info and message logs, which remain within your defined VPC boundaries.

Use it with your favorite AI tools

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