Can the AI clear the cache when triggering a deploy?

Yes, absolutely. The tool `trigger_deploy` incorporates an optional variable explicitly created for cache management. You can command the agent: "Redeploy the web app named Node-Backend and bypass rendering cache."

Which type of new services can the AI deploy using `create_service`?

The MCP can provision and launch exactly three core resource forms utilizing GitHub repos: standard web services (`web_service`), private network-locked processes (`private_service`), and asynchronous task handlers (`background_worker`).

Warning: Is there a confirmation before using `delete_service`?

Since natural language agents can occasionally misinterpret parameters, invoking the text request explicitly will route straight to the Render API resulting in instantaneous destruction. Please ensure absolute clarity when pointing the AI logic toward deletion operations.

Render MCP Server for Google ADK 10 tools — connect in under 2 minutes

Name: Render
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Render as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="render_agent",
    instruction=(
        "You help users interact with Render "
        "using 10 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

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High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Render MCP Server

Connect your AI assistant directly to your Render cloud infrastructure via their official capabilities API. By granting your agent access to your hosting environments, you transform standard chat text into a powerful DevOps control center. Command deployments, scale back background workers to save costs, and instantiate brand-new services linked directly from your GitHub repositories without ever opening the Render dashboard.

Google ADK natively supports Render as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 10 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Control Services & Spend — Retrieve status checks on all active web endpoints, databases, and cron jobs (list_services). Instantly pause compute on unused projects using suspend_service and wake them back up later with resume_service to manage hosting costs.
Trigger & Monitor Deployments — Inspect the deployment history for a specific application (list_deploys). Noticed a hotfix on GitHub? Tell your AI to forcefully restart the build pipeline executing trigger_deploy while optionally clearing the build cache.
Architect Environments — Direct the agent to dynamically provision fresh infrastructure (create_service) pointing to a specific GitHub repository branch. Or easily swap which branch an existing project trails using update_service_branch.
Clean Up Infrastructure — Quickly tear down obsolete staging instances permanently by instructing the AI via natural language to purge unwanted resources (delete_service).

The Render MCP Server exposes 10 tools through the Vinkius. Connect it to Google ADK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Render to Google ADK via MCP

Follow these steps to integrate the Render MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 10 tools from Render via MCP

Why Use Google ADK with the Render MCP Server

Google ADK provides unique advantages when paired with Render through the Model Context Protocol.

Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Render

Production-ready features like session management, evaluation, and deployment come built-in. not bolted on

Seamless integration with Google Cloud services means you can combine Render tools with BigQuery, Vertex AI, and Cloud Functions

Render + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Render MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Render and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Render tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Render regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Render

Render MCP Tools for Google ADK (10)

These 10 tools become available when you connect Render to Google ADK via MCP:

create_service

Specify type, name, owner, and repository. Creates a new Render service from a GitHub repository

delete_service

This action is irreversible. Permanently deletes a Render service

get_deploy

Retrieves details for a specific deployment

get_service

Retrieves details for a specific Render service

list_deploys

Lists recent deployments for a service

list_services

Lists all services (web apps, databases, cron jobs) in the Render account

resume_service

Resumes a previously suspended service

suspend_service

Suspends a service to stop execution and billing

trigger_deploy

Triggers a manual deployment for a service

update_service_branch

Updates the tracked GitHub branch for a service

Example Prompts for Render in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Render immediately.

"List my web services, then suspend the one named 'old-staging-app'."

"Check the recent deployment history for my main front-end service (srv-xyz123)."

"Trigger a force deployment on service ID 'srv-backend88' and clear its build cache."

Troubleshooting Render MCP Server with Google ADK

Common issues when connecting Render to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Render + Google ADK FAQ

Common questions about integrating Render MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.