How to Use the Hiver MCP in Google ADK

Q: Can a Google ADK agent assign emails to specific team members in Hiver?

Yes. The agent can use searchteammembers to locate the correct user and then call updatethreadstatus to assign the conversation.

Q: Does Hiver work with the Gemini long-context window in Google ADK?

Yes. You can fetch complete thread histories using getconversationdetails and feed the entire text block into Gemini to generate highly contextual drafts.

Q: How do I restrict which tools my Gemini agent can access on this MCP Server?

Use the optional toolnames filter in the McpToolset configuration. This lets you limit the agent to harmless tools like listinboxtags while blocking write tools if needed.

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Connect Hiver MCP to Google ADK

Create your Vinkius account to connect Hiver 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 Hiver with Google ADK

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Analyze high-volume shared inboxes with Google ADK

This MCP Server connects your enterprise Gemini models to Hiver via `list_shared_inboxes` and `get_inbox_details` to analyze support volume. Because Gemini handles massive context, you can feed entire inbox structures directly into your reasoning loops. The agent reads the list of conversations and uses BigQuery data to prioritize which threads need immediate attention. This lets your Google ADK agent make data-backed routing decisions inside your workspace.

Draft contextual email replies from Gemini insights

The `get_conversation_details` tool retrieves the full history of any email thread for deep analysis. Your agent can read the entire thread, cross-reference it with internal databases, and formulate a reply. Once the reply is ready, the agent calls `create_shared_draft` to save the message as a draft. Your support team can then open Gmail and send the pre-written response with a single click.

Automate tag management inside your Google ADK agent

The `list_inbox_tags` and `search_tags_by_name` tools let your agent categorize incoming support requests. Your agent matches customer issues to your existing Google Workspace labels without human intervention. By combining tag search with `update_thread_status`, the agent can mark threads as pending or closed. This keeps your Hiver workspace clean and ensures your SLA metrics remain accurate.

Setup guide

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

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Common questions about Hiver MCP in Google ADK

Initialize the McpToolset with the streamable HTTP parameters pointing to your Vinkius endpoint. Pass this toolset directly to your LlmAgent to expose the email management tools.

Yes. The agent can use `search_team_members` to locate the correct user and then call `update_thread_status` to assign the conversation.

Yes. You can fetch complete thread histories using `get_conversation_details` and feed the entire text block into Gemini to generate highly contextual drafts.

Use the optional tool_names filter in the McpToolset configuration. This lets you limit the agent to harmless tools like `list_inbox_tags` while blocking write tools if needed.

All email text and metadata are processed in memory within secure, ephemeral V8 isolates. No incoming email data is written to disk or used for model training, keeping your company communications secure.

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