How to Use the Flightcontrol (AWS PaaS Deployments) MCP in Google ADK

Q: Can I filter tools when using Flightcontrol (AWS PaaS Deployments) with Google ADK?

Yes, you can use the toolnames parameter in your configuration to restrict which functions your agent can access. This limits the agent to only the operations you permit.

Q: How do I trigger deployments in Google ADK?

Simply call createdeployment with your repository filters. The agent manages the request and monitors the status until completion.

Control AWS infrastructure with Flightcontrol (AWS PaaS Deployments) inside Google ADK.

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Connect Flightcontrol (AWS PaaS Deployments) MCP to Google ADK

Create your Vinkius account to connect Flightcontrol (AWS PaaS Deployments) 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 Flightcontrol (AWS PaaS Deployments) with Google ADK

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Sync AWS with Google ADK

Connect your infrastructure management to your existing Google Cloud workflows. Use `list_projects` to discover your active stacks and `get_aws_account_details` to verify connectivity status. This bridges your AWS management with the data-heavy reasoning of your Gemini agents. You get a unified view of your entire technical stack.

Execute one-off jobs

Run maintenance tasks or data migrations on your AWS infrastructure. Use `create_job_execution` to trigger specific background processes and `get_job_execution_status` to track progress. Your agent can trigger these jobs based on analysis of your database state. It keeps the infrastructure clean without you needing to log in to the AWS console.

Manage cache invalidations

Clear your edge caches after code deployments. Use `create_cloudfront_invalidation` to purge stale assets and `get_cloudfront_invalidation_status` to confirm the cache is fresh. Your agent automates this final step of the release process. It ensures your users see the latest version of your application immediately after a push.

Setup guide

Set up Flightcontrol (AWS PaaS Deployments) 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 Flightcontrol (AWS PaaS Deployments) 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="Flightcontrol (AWS PaaS Deployments)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Flightcontrol (AWS PaaS Deployments) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Flightcontrol (AWS PaaS Deployments) MCP in Google ADK

Use the McpToolset class to define your server connection. Pass this toolset into your LlmAgent to start executing commands against your infrastructure.

Yes, you can use the tool_names parameter in your configuration to restrict which functions your agent can access. This limits the agent to only the operations you permit.

The server provides concise, structured responses that fit perfectly into large context windows. This allows your agent to reason over your entire AWS project history.

Simply call `create_deployment` with your repository filters. The agent manages the request and monitors the status until completion.

We keep your account telemetry isolated within an ephemeral sandbox. Only the minimal project metadata required for tool execution is ever accessed by the server.

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