How to Use the AdaptiveWork (Clarizen) MCP in Google ADK

Q: How does Google ADK leverage Gemini's long context with AdaptiveWork (Clarizen) data?

The toolset feeds raw JSON arrays from listtasks directly into Gemini's context window. The model processes thousands of task lines at once, identifying systemic project delays that smaller models miss.

Connect Google ADK to AdaptiveWork to drive massive enterprise project updates using Gemini's million-token context.

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Connect AdaptiveWork (Clarizen) MCP to Google ADK

Create your Vinkius account to connect AdaptiveWork (Clarizen) 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 AdaptiveWork (Clarizen) with Google ADK

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Ingest massive portfolio lists using Google ADK and MCP

The `list_projects` tool feeds your Gemini models with complete lists of active enterprise initiatives directly from Clarizen. Because Google ADK supports deep reasoning over massive token windows, you can feed hundreds of project records into a single prompt. Your agent uses `get_project_details` to pull granular progress metrics, then combines this with external datasets stored in BigQuery. The ADK framework coordinates these tools natively, letting you build complex data-enrichment pipelines on Google Cloud.

Inject live task updates into Gemini agent loops

The `create_task` tool translates high-level planning emails or Slack pings into structured Clarizen tasks automatically. Your Google ADK agent parses incoming requests, identifies the parent project, and writes the task name and parent ID back to AdaptiveWork. To verify that dependencies are respected, the agent calls `list_tasks` to inspect the existing work breakdown structure. This prevents duplicate schedules and ensures your project hierarchy remains clean.

Query resource assignments using CZQL and Google ADK

The `run_query` tool grants your Gemini-powered agent the ability to run raw Clarizen Query Language commands. This lets the agent search across complex resource tables that standard endpoints cannot easily filter. By combining this query power with `list_users`, the agent matches active project demands with real-time team availability. The toolset exposes these resources directly to your cloud-hosted agent without requiring manual exports.

Setup guide

Set up AdaptiveWork (Clarizen) 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 AdaptiveWork (Clarizen) 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="AdaptiveWork (Clarizen)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to AdaptiveWork (Clarizen) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about AdaptiveWork (Clarizen) MCP in Google ADK

Install the google-adk package and initialize the McpToolset with your unique Vinkius HTTP URL. Pass this toolset directly to your LlmAgent instance to expose the six project management tools to Gemini.

Yes, you can pass a list of allowed tool names to the McpToolset constructor to restrict access. This lets you block write operations like `create_task` in environments where you only want read-only capabilities.

The toolset feeds raw JSON arrays from `list_tasks` directly into Gemini's context window. The model processes thousands of task lines at once, identifying systemic project delays that smaller models miss.

Yes, the ADK supports both standard input-output transport for local testing and HTTP transport for cloud engines. Vinkius hosts the MCP Server, so your cloud-deployed agents connect via a single secure endpoint.

Your resource names, project IDs, and task hierarchies flow strictly through secure HTTPS tunnels to Vinkius ephemeral sandboxes. No project data is stored permanently on the proxy, keeping your enterprise schedules private.

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