How to Use the Factorial MCP in Google ADK

Q: How do I add Factorial to my Google ADK agent?

You initialize the McpToolset with the server URL and pass it into your LlmAgent tools list. It handles the communication between your agent and the Factorial endpoints.

Q: Is there a way to limit Factorial access in Google ADK?

You can use the toolnames filter when setting up the McpToolset. This lets you restrict the agent to only the specific tools it needs for its job.

Integrate Factorial HR tools into your Google ADK pipelines for enterprise-grade automation across your cloud infrastructure.

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Connect Factorial MCP to Google ADK

Create your Vinkius account to connect Factorial 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 Factorial with Google ADK

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Enterprise HR automation with Google ADK

Bring your Factorial team data into your Gemini-powered workflows. Use `list_teams` and `list_employees` to map your organizational structure directly inside your Google Cloud environment. Your agents gain the ability to reason over this data at scale. Because it connects via `McpToolset`, you can filter which tools are exposed to specific agents.

Streamlined document and folder discovery

Search through your company files using `list_documents` and `list_folders`. Your agent identifies the right file paths and references them within your broader data processing tasks. This provides a clear trail for document management. Your agent performs these queries as part of a larger pipeline, keeping everything centralized in your Google ADK setup.

Calendar and leave management

Query `list_leaves` and `list_holidays` to build awareness of team availability. Your agent uses these outputs to schedule tasks or coordinate project timelines based on real HR data. It works out of the box with your existing Google infrastructure. You get a direct feed of leave data without needing to build custom API wrappers for Factorial.

Setup guide

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

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Common questions about Factorial MCP in Google ADK

You initialize the `McpToolset` with the server URL and pass it into your `LlmAgent` tools list. It handles the communication between your agent and the Factorial endpoints.

Yes, the server supports both Stdio and HTTP transports. You can choose the one that fits your deployment architecture on Google Cloud.

You can use the tool_names filter when setting up the `McpToolset`. This lets you restrict the agent to only the specific tools it needs for its job.

The framework handles the MCP protocol communication. If your queries return large sets of data, the Gemini models can process them thanks to their high token capacity.

The server communicates over a secure channel using your provided endpoint token. The data stays within the request-response cycle of your agent, and you control the scope of the API key.

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