Can the AI forcefully terminate or delete critical production endpoint fleets on demand?

No. This module safely allows the AI to only pause and manage running instances. Destructive deletion actions (like completely erasing a pod) are intentionally prohibited by the tooling design to protect your critical compute resources from unintended loss.

Can the AI provision large GPU arrays automatically?

Yes. Using the `create_pod` capability, the AI can query the available hardware models (such as A100 or H100) and immediately launch new Docker clusters based on existing community templates, simplifying complex DevOps scaling actions significantly.

Will the AI know the billing state or the real-time cost of running each endpoint?

No. The current RunPod AI module is concentrated on operational control and system orchestration, such as discovering inactive processes and booting new instances. Deep billing analytics or invoice extraction is not natively integrated in the commands exposed to the AI at this time.

RunPod MCP Server for Google ADK 7 tools — connect in under 2 minutes

Name: RunPod
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 7 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add RunPod 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="runpod_agent",
    instruction=(
        "You help users interact with RunPod "
        "using 7 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

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 RunPod MCP Server

Connect your AI directly to RunPod, the leading cloud infrastructure provider for on-demand GPU computing and serverless execution. Empower your conversational agent to act as a highly proficient DevOp engineer, managing advanced computational workloads, exploring deployment options, and spinning up new hardware instances.

Google ADK natively supports RunPod as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 7 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

Manage Pods On-Demand — Effortlessly identify running and paused GPU machines across your cloud account (list_pods, get_pod). Halt specific billable instances to control costs securely (stop_pod).
Provision GPU Workloads — Find verified templates or specific GPU architectures ready for deployment (list_templates, list_gpu_types), and create entirely new hardware nodes immediately directly from chat (create_pod).
Audit Serverless Environments — Review all registered endpoints routing your containerized inference applications (list_endpoints).

The RunPod MCP Server exposes 7 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 RunPod to Google ADK via MCP

Follow these steps to integrate the RunPod 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 7 tools from RunPod via MCP

Why Use Google ADK with the RunPod MCP Server

Google ADK provides unique advantages when paired with RunPod 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 RunPod

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 RunPod tools with BigQuery, Vertex AI, and Cloud Functions

RunPod + Google ADK Use Cases

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

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

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

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

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

RunPod MCP Tools for Google ADK (7)

These 7 tools become available when you connect RunPod to Google ADK via MCP:

create_pod

Specify name, GPU type, and Docker image. Creates a new GPU pod

get_pod

Retrieves details for a specific GPU pod

list_endpoints

Lists all serverless endpoints

list_gpu_types

Lists available GPU hardware types

list_pods

Lists all GPU pods in the account

list_templates

Lists saved pod templates

stop_pod

Stops a running GPU pod

Example Prompts for RunPod in Google ADK

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

"Show me our stopped GPU pods."

"Check what GPU templates are available to deploy a new Llama-3 inference instance."

"Pause pod with ID 'pod_xyz_980' immediately to prevent recurring costs throughout the evening."

Troubleshooting RunPod MCP Server with Google ADK

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

McpToolset not found

Update: pip install --upgrade google-adk

RunPod + Google ADK FAQ

Common questions about integrating RunPod 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.