How to Use the TaskForce MCP in Google ADK

Q: How does Google ADK handle TaskForce workflows?

The agent processes the TaskForce tools by executing them within your existing Google Cloud infrastructure. It treats data from listtaskforcecases and other sources as one single, connected dataset. This means the analysis is grounded in verifiable BigQuery truth.

Q: Can I use TaskForce quotes in Google ADK?

Yes. You call listtaskforcequotes to retrieve all current pricing proposals. The agent can then compare these against active customer data pulled from the same ecosystem. This makes it easy to spot mismatched or outdated quote information.

Q: What is the best way to manage leads with TaskForce and the Google ADK?

First, use listtaskforceleads to get an initial volume count. Next, if a lead looks promising, run createtaskforcecase. The agent can then analyze the case details against historical data for better qualification scoring.

Q: If I need customer info in TaskForce and Google ADK, which tools should I use?

Start by using gettaskforcecustomer to get a specific user's current details. Then, if you also want their billing history, run listtaskforceinvoices. This gives the agent all necessary context for decision-making.

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Connect TaskForce MCP to Google ADK

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Setup TaskForce with Google ADK

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Analyze customer records.

The `get_taskforce_customer` tool pulls a full record for any specified user. Because your agent connects to BigQuery, it can correlate this data with years of historical usage metrics. For an overview, use `list_taskforce_customers` when you need the roster of all accounts in Google Cloud.

Process and generate sales quotes.

If a deal is closing, use `create_taskforce_case` to log the effort. The agent can then sequence this action with generating a quote using `list_taskforce_quotes`. This workflow ensures all necessary internal documentation exists before sending pricing details.

Review and manage lead volume.

Need to know what leads are sitting unassigned? Run `list_taskforce_leads` to pull the raw list. Gemini models can then process this large dataset (1M+ tokens) to identify patterns or decay risks. This level of data context is key for building enterprise-grade sales pipelines.

Setup guide

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

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Common questions about TaskForce MCP in Google ADK

The agent processes the `TaskForce` tools by executing them within your existing Google Cloud infrastructure. It treats data from `list_taskforce_cases` and other sources as one single, connected dataset. This means the analysis is grounded in verifiable BigQuery truth.

The server touches customer and lead records. This includes names, contact information, associated cases, and billing details. Since you're on Google Cloud, this data is governed by enterprise-level compliance standards. The agent can process the sensitive account information with high security.

Yes. You call `list_taskforce_quotes` to retrieve all current pricing proposals. The agent can then compare these against active customer data pulled from the same ecosystem. This makes it easy to spot mismatched or outdated quote information.

First, use `list_taskforce_leads` to get an initial volume count. Next, if a lead looks promising, run `create_taskforce_case`. The agent can then analyze the case details against historical data for better qualification scoring.

Start by using `get_taskforce_customer` to get a specific user's current details. Then, if you also want their billing history, run `list_taskforce_invoices`. This gives the agent all necessary context for decision-making.

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