How to Use the Digify MCP in Google ADK

Q: How do I register the Digify MCP Server with the Google ADK?

Install the package using pip install google-adk and define your toolset using McpToolset. Pass your Vinkius HTTP endpoint into the server parameters, then add the toolset to your LlmAgent tools array.

Q: Can I restrict which Digify tools my Google ADK agent can access?

Yes, you can use the toolnames filter when initializing your McpToolset. This lets you restrict the agent to read-only tools like listfiles and getfile, blocking write operations.

Q: How does Google ADK handle long-context analysis of Digify logs?

The ADK passes raw JSON output from listactivities directly to Gemini. Because of the model's 1M+ token capacity, you can analyze entire history logs of data room interactions in a single logical step.

Q: Where does my document and data room configuration data go when using Google ADK with Digify?

Your data room structures, file details fetched via getfile, and webhook configs from listwebhooks are processed within Vinkius's secure MCP sandbox. This metadata is never used to train Gemini models, and the transport layer uses encrypted HTTP connections to keep your enterprise file lists private.

Feed document access logs directly into Gemini models using Google ADK and our managed MCP Server to analyze activity.

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Connect Digify MCP to Google ADK

Create your Vinkius account to connect Digify 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 Digify with Google ADK

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Audit large-scale document access with Google ADK

The `list_activities` tool feeds complete document action histories directly into Gemini's 1M+ token context window. Your Google ADK agent parses months of viewer logs in a single prompt, identifying security risks that shorter-context models miss. You connect this data directly to your enterprise pipeline by passing the MCP toolset to your `LlmAgent`. The agent queries `get_dataroom_stats` to cross-reference file views with your existing customer tables in BigQuery.

Manage data room guests via Gemini reasoning

The `invite_guest` tool adds external collaborators to specific secure folders based on natural language instructions. Your agent evaluates incoming access requests from your database and runs this tool to grant entry instantly. By configuring `McpToolset` with your Vinkius HTTP URL, the Gemini agent gains direct access to `list_dataroom_guests`. The model reasons over the active guest list and prunes inactive users without requiring manual script updates.

Automate file protection in Vertex AI workflows

The `protect_file` tool secures raw documents before they are distributed to external partners or public cloud buckets. Your Vertex AI pipeline uses this tool to apply dynamic watermarks to files generated during automated batch runs. You restrict which tools the model can access by using the optional `tool_names` filter in the ADK setup. This ensures your agent can execute `list_files` but cannot modify webhooks via `list_webhooks` unless explicitly authorized.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Digify. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Digify MCP in Google ADK

Install the package using `pip install google-adk` and define your toolset using `McpToolset`. Pass your Vinkius HTTP endpoint into the server parameters, then add the toolset to your `LlmAgent` tools array.

Yes, you can use the `tool_names` filter when initializing your `McpToolset`. This lets you restrict the agent to read-only tools like `list_files` and `get_file`, blocking write operations.

The ADK passes raw JSON output from `list_activities` directly to Gemini. Because of the model's 1M+ token capacity, you can analyze entire history logs of data room interactions in a single logical step.

Absolutely. Your agent can query BigQuery for a list of active clients, then use `create_dataroom` to set up matching secure environments, linking your cloud data warehouse directly to your document security workflows.

Your data room structures, file details fetched via `get_file`, and webhook configs from `list_webhooks` are processed within Vinkius's secure MCP sandbox. This metadata is never used to train Gemini models, and the transport layer uses encrypted HTTP connections to keep your enterprise file lists private.

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