How to Use the Cradl AI MCP in Google ADK

Q: Is Google ADK compatible with Cradl AI batch functions?

Absolutely. You can call listbatches and getbatchdetails to monitor your document processing status directly from your Google Cloud agent.

Connect Cradl AI to your Google ADK environment for high-volume document processing on Google Cloud infrastructure.

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Connect Cradl AI MCP to Google ADK

Create your Vinkius account to connect Cradl AI 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 Cradl AI with Google ADK

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Execute document flows with Google ADK

Your agent calls `list_workflows` to see available routing rules. It then triggers specific flows to handle incoming documents automatically. `get_flow_details` allows your agent to understand the configuration of each process. It ensures your document routing matches your current business logic.

Scale document ingestion with Google ADK

Use `list_batches` to organize your document intake. Your agent manages batch identifiers to keep track of large document sets. `get_batch_details` provides a summary of the processing progress for every file. You maintain full oversight of your data pipeline.

Search model metadata in Google ADK

Run `search_models_by_name` to find the exact model required for your document type. Your agent locates the right tool based on your specific keywords. This keeps your agent flexible when dealing with diverse document formats. It identifies the best extraction logic on the fly.

Setup guide

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

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Common questions about Cradl AI MCP in Google ADK

Initialize the McpToolset with your server URL. Add this toolset to your LlmAgent to give your agent immediate access to the Cradl AI extraction suite.

Absolutely. You can call `list_batches` and `get_batch_details` to monitor your document processing status directly from your Google Cloud agent.

It can. By extracting structured data from documents first, you feed cleaner information into your Gemini context window. This makes your long-context reasoning much more precise.

It supports both Stdio and HTTP transports. You can choose the connection method that best fits your Google Cloud infrastructure requirements.

We enforce strict encryption for all document payloads in transit. Your extracted key-value pairs are transient and isolated from other user sessions within our sandbox.

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