How to Use the Habitify MCP in Google ADK

Q: Can I restrict which tools Habitify exposes to Google ADK?

You can use the toolnames filter in your toolset definition. This limits the agent to only the specific functions you want to permit.

Q: Does Google ADK work with long-context Habitify logs?

Absolutely. Gemini models handle the output of listhabitlogs efficiently, allowing the agent to reason over long-term trends.

Integrate Habitify into your Google ADK enterprise agents for automated tracking across your cloud infrastructure.

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Connect Habitify MCP to Google ADK

Create your Vinkius account to connect Habitify 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 Habitify with Google ADK

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Perplexity

Syncing Habitify with Google ADK workflows

Your agent uses `create_habit` to build new routines based on project deadlines in your environment. It keeps your work and personal goals aligned by reading from the same source of truth.

Complex data retrieval for Google ADK

Call `get_journal` to retrieve your full daily overview. The agent processes this list to identify gaps in your schedule. You get a clear view of your day without manual checking.

Direct habit modification using toolsets

Use `update_habit` to change frequencies or names as your goals shift. The change pushes to your account instantly. Your agent maintains your tracking logic so you don't have to manage it yourself.

Setup guide

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

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Why Choose Vinkius

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Common questions about Habitify MCP in Google ADK

Install the toolset package and define your server parameters using `StreamableHttpServerParameters`. Pass this toolset into your `LlmAgent` initialization.

You can use the tool_names filter in your toolset definition. This limits the agent to only the specific functions you want to permit.

Absolutely. Gemini models handle the output of `list_habit_logs` efficiently, allowing the agent to reason over long-term trends.

The agent framework logs all tool interactions. You can view the full request and response history for every `get_habit_stats` call in your cloud logs.

Your credentials remain isolated within the Vinkius sandbox. The agent only interacts with your habit data through the authenticated MCP interface.

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