How to Use the HelpCrunch MCP in Google ADK

Q: Can Google ADK filter which HelpCrunch tools are exposed?

Yes. You can apply a toolnames filter when setting up the toolset. This restricts the agent to specific actions like getconversationdetails while blocking write access.

Q: Can the agent change a ticket's status?

It calls updateconversationstatus to mark chats as open, pending, or closed. This keeps your queue clean without human intervention.

Connect HelpCrunch to Google ADK to feed millions of tokens of live chat data directly into your Gemini enterprise agents.

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Connect HelpCrunch MCP to Google ADK

Create your Vinkius account to connect HelpCrunch 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 HelpCrunch with Google ADK

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Perplexity

Massive context windows

The `list_conversations` and `list_messages_in_chat` tools dump massive amounts of ticket history straight into Gemini. Google ADK thrives on huge context windows. You can feed a customer's entire lifetime chat history into the model in one go. This means zero lost context. The agent reads every interaction before deciding how to respond. It then uses `send_chat_message` to reply with pinpoint accuracy, drawing on past issues without asking the user to repeat themselves.

Enterprise MCP Server auditing

The `list_active_webhooks` tool lets your agent audit your support infrastructure. Large teams often lose track of where their event triggers are pointing. The agent pulls the active configurations so you can verify them against your Google Cloud architecture. You combine this with `list_departments` to map out your routing rules. The ADK agent analyzes the webhook destinations and department structures to ensure incoming tickets flow to the right endpoints.

Actionable customer profiles

The `create_customer` tool allows your Gemini agent to instantly register new users mid-conversation. If an unregistered visitor starts asking complex questions, the agent captures their name and email, then builds the profile on the fly. Later, the system retrieves that exact profile via `search_customers`. By connecting this flow to Vertex AI, you can run advanced sentiment analysis on the user's queries and attach those insights to their permanent record.

Setup guide

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

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Common questions about HelpCrunch MCP in Google ADK

Use McpToolset with StreamableHttpServerParameters pointing to your Vinkius URL. Pass the resulting toolset into your LlmAgent tools array.

Yes. You can apply a tool_names filter when setting up the toolset. This restricts the agent to specific actions like get_conversation_details while blocking write access.

It works perfectly. You can pull thousands of messages using list_messages_in_chat and Gemini will process the entire transcript without dropping details.

It calls update_conversation_status to mark chats as open, pending, or closed. This keeps your queue clean without human intervention.

The integration reads raw message payloads and user names via list_messages_in_chat. Vinkius handles this through a zero-trust architecture, meaning no request data is ever written to persistent storage.

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