Which LLM providers does Portkey support?

Portkey supports 1,600+ LLMs including OpenAI, Anthropic, Google, Mistral, Azure OpenAI, AWS Bedrock, Cohere, Hugging Face, and many more. Use the list_models tool to see the full catalog available via your gateway.

How does Portkey help control AI costs?

Portkey provides granular visibility into token usage, latency, and costs per model, team, or virtual key. You can create budget policies with hard limits to prevent runaway spending. The gateway also supports caching to reduce duplicate calls and fallbacks to cheaper models when appropriate.

Can I track feedback on AI responses?

Yes! Portkey allows you to submit Like/Dislike feedback for any logged LLM call. This data helps improve model selection, evaluate agent performance, and build RLHF datasets for fine-tuning.

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

Name: Portkey
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 Portkey as an MCP tool provider through the 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="portkey_agent",
    instruction=(
        "You help users interact with Portkey "
        "using 10 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

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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 Portkey MCP Server

What you can do

Connect AI agents to the Portkey AI Gateway for enterprise-grade observability and management:

Google ADK natively supports Portkey as an MCP tool provider — declare the 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.

Monitor logs and traces of all LLM calls passing through your gateway
Analyze token usage, latency, and costs across models and teams
Submit feedback (Likes/Dislikes) to improve model quality and agent performance
Export logs for audit trails, compliance, and offline cost analysis
Review gateway configurations including retry policies, fallbacks, and cache settings
Manage virtual keys to track provider API key usage and limits
Discover supported models from 1,600+ LLMs available via Portkey
Enforce budget policies to prevent runaway AI costs per team or project

The Portkey 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 Portkey to Google ADK via MCP

Follow these steps to integrate the Portkey 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 Portkey via MCP

Why Use Google ADK with the Portkey MCP Server

Google ADK provides unique advantages when paired with Portkey 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 Portkey

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 Portkey tools with BigQuery, Vertex AI, and Cloud Functions

Portkey + Google ADK Use Cases

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

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

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

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

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

Portkey MCP Tools for Google ADK (10)

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

create_policy

Requires policy name, budget limit (USD or token count), and optionally the target users or virtual keys to restrict. Returns the created policy details. Use this to enforce cost controls on specific teams or projects using the gateway. Create a new budget or usage policy for AI gateway access

delete_policy

Requires the policy ID. Use this when a project ends or budget constraints are no longer needed. Remove a budget or usage policy from Portkey

export_logs

Optionally filters by date range, model, or user. Returns an export ID or download URL. Use this for audit trails, cost reporting, or offline analysis of AI usage patterns. Export AI gateway logs for external analysis or compliance reporting

get_log_details

Requires the log ID from list_logs results. Use this for deep debugging of specific AI interactions. Get detailed information about a specific AI gateway log entry

get_virtual_keys

Virtual keys map to underlying provider keys (OpenAI, Anthropic, etc.) with metadata, usage limits, and policy associations. Returns key IDs, names, provider targets, current usage, and status. Use this to audit API key usage or identify keys approaching limits. List all virtual API keys managed by Portkey

list_configs

Returns config IDs, names, creation dates, and associated virtual keys. Use this to review how LLM requests are routed or to audit gateway behavior. List all gateway configurations stored in Portkey

list_logs

Returns log IDs, timestamps, model names, token usage, latency, costs, and status codes. Use this to monitor AI usage, identify expensive calls, or debug latency issues. Supports pagination via limit/offset. List recent AI gateway logs and traces from Portkey

list_models

). Returns model names, provider names, supported endpoints (chat, embeddings, etc.), and capabilities. Use this to discover which models are routable via your gateway. List all LLM models supported by the Portkey gateway

list_policies

Returns policy names, limits, current consumption, and affected users/keys. Use this to review guardrails preventing runaway AI costs. List all budget and usage policies defined in Portkey

submit_feedback

Requires the log ID, rating (LIKE, DISLIKE, or UNLIKE to remove), and optional text feedback. Use this to build RLHF datasets or monitor user satisfaction with AI outputs. Submit user feedback (Like/Dislike) for a specific AI response log

Example Prompts for Portkey in Google ADK

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

"Show me the most expensive LLM calls from the last 24 hours"

"Create a budget policy limiting the Marketing team to $500/month on LLM usage"

"Export all logs from last week for our compliance audit"

Troubleshooting Portkey MCP Server with Google ADK

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

McpToolset not found

Update: pip install --upgrade google-adk

Portkey + Google ADK FAQ

Common questions about integrating Portkey 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.