How to Use the cloudlayer.io MCP in Google ADK

Q: How do I configure the cloudlayer.io MCP Server inside Google ADK?

Install the SDK with pip install google-adk. Instantiate McpToolset using your Vinkius HTTP URL, then pass that toolset object into your LlmAgent tools array to expose the document generation functions.

Q: Can I restrict which cloudlayer.io tools my Google ADK agent can access?

Yes. Use the optional toolnames filter when initializing your McpToolset to expose only the specific tools you want, like limiting the agent to captureurlscreenshot while blocking usage statistics.

Q: How does cloudlayer.io handle large HTML payloads sent from Gemini?

The converthtmltopdf tool processes large HTML strings efficiently. Since Gemini can generate highly detailed markup, the HTTP transport handles these payloads without hitting token or buffer limits.

Q: Can my agent monitor document generation costs using this MCP toolset?

Yes. Your agent can run getcloudlayerusagestats to verify your remaining generation quota before kicking off batch processing jobs, preventing unexpected API overages.

Convert BigQuery data into executive-ready PDFs using Gemini and the Google ADK MCP Server.

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Connect cloudlayer.io MCP to Google ADK

Create your Vinkius account to connect cloudlayer.io 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 cloudlayer.io with Google ADK

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Turn massive context into PDFs with Google ADK

Gemini's million-token context window lets your agent analyze massive datasets pulled straight from BigQuery. Once the analysis is complete, the agent uses `convert_html_to_pdf` to write that deep intelligence into a clean, formatted executive report. If the agent finds external data sources during its search, it captures them instantly. The agent triggers `convert_url_to_pdf` to append whole reference web pages to the final report, keeping all source materials intact.

Visual archiving of web assets

Keep a visual record of your live web applications directly inside your Google Cloud storage pipelines using this MCP Server. Your agent executes `capture_url_screenshot` to take snapshots of dashboard states based on alerts triggered in your cloud monitoring tools. To keep track of these operations, the agent queries `list_generation_history` to index every snapshot. This creates a searchable log of visual states that your agent can reference during compliance audits.

Dynamic templating for enterprise reports

Avoid hardcoded report layouts by letting Gemini select the best format for your data. Your agent calls `list_pdf_templates` to find active Nunjucks layouts stored in your account, matching the layout to the specific data structure it retrieved. The agent then uses `get_template_configuration` to inspect the layout schema. It populates the template variables with parsed BigQuery rows, generating polished corporate PDFs without manual template mapping.

Setup guide

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

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Common questions about cloudlayer.io MCP in Google ADK

Install the SDK with `pip install google-adk`. Instantiate `McpToolset` using your Vinkius HTTP URL, then pass that toolset object into your `LlmAgent` tools array to expose the document generation functions.

Yes. Use the optional `tool_names` filter when initializing your `McpToolset` to expose only the specific tools you want, like limiting the agent to `capture_url_screenshot` while blocking usage statistics.

The `convert_html_to_pdf` tool processes large HTML strings efficiently. Since Gemini can generate highly detailed markup, the HTTP transport handles these payloads without hitting token or buffer limits.

Yes. Your agent can run `get_cloudlayer_usage_stats` to verify your remaining generation quota before kicking off batch processing jobs, preventing unexpected API overages.

The HTML strings and URL assets processed by this MCP Server are sent directly to the generation API over HTTPS and are not stored permanently. The V8 sandbox ensures that temporary rendering files are destroyed immediately after delivery.

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