How to Use the Sunsama MCP in Google ADK

Q: Can I programmatically manage tasks with Sunsama using the Google ADK?

Yes. The agent supports modifying tasks via updatetask and creating new ones with createtask. You pass these actions through the structured tools provided by the MCP Server.

Q: What are the steps for deleting work items in Sunsama with the Google ADK?

The agent exposes deletetask to your workflow. Since this is an irreversible action, the system prompts you and logs the event within your connected Google Cloud infrastructure.

Act on Sunsama data in BigQuery with the Google ADK.

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Connect Sunsama MCP to Google ADK

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Setup Sunsama with Google ADK

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Get task details using `get_task_details`

The agent retrieves deep information for a single item via `get_task_details`. This is critical when you need to cross-reference a specific task with data sitting in BigQuery. The long context reasoning of Gemini models can hold this detail alongside millions of other records, making the connection seamless.

List all tasks and contexts via `list_tasks` or `list_contexts`

You can pull a comprehensive list of your outstanding work using `list_tasks`. Need to know what projects exist? Use `list_contexts` first. This provides the necessary framework for deep analysis. The Google ADK lets you reason over these lists, connecting task status directly to underlying enterprise data sources.

List available channels with `list_channels`

The agent pulls all defined Sunsama communication channels (like 'Work' or 'Personal') using `list_channels`. This defines the boundaries for your project teams. When integrating into Google Cloud, knowing these channel boundaries helps ensure that agents only process data relevant to specific departments.

Setup guide

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

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Common questions about Sunsama MCP in Google ADK

The framework supports long-context reasoning (1M+ tokens). You can feed a massive amount of historical data into Gemini, and then ask it to act on a specific task or profile in Sunsama.

Yes. The agent supports modifying tasks via `update_task` and creating new ones with `create_task`. You pass these actions through the structured tools provided by the MCP Server.

You can run a query that aggregates data from BigQuery and then uses `list_tasks` to identify potential conflicts. The agent doesn't just list; it reasons about the implications of the listed tasks.

The agent exposes `delete_task` to your workflow. Since this is an irreversible action, the system prompts you and logs the event within your connected Google Cloud infrastructure.

This MCP Server touches task details (text/date), user profiles, channels, contexts, and general project assignments. All this structured data can be mapped against your BigQuery tables.

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