Portkey MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Portkey through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Portkey Assistant",
instructions=(
"You help users interact with Portkey. "
"You have access to 10 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Portkey"
)
print(result.final_output)
asyncio.run(main())
* 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:
The OpenAI Agents SDK auto-discovers all 10 tools from Portkey through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Portkey, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
- 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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Portkey MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 10 tools from Portkey
Why Use OpenAI Agents SDK with the Portkey MCP Server
OpenAI Agents SDK provides unique advantages when paired with Portkey through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Portkey + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Portkey MCP Server delivers measurable value.
Automated workflows: build agents that query Portkey, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Portkey, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Portkey tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Portkey to resolve tickets, look up records, and update statuses without human intervention
Portkey MCP Tools for OpenAI Agents SDK (10)
These 10 tools become available when you connect Portkey to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Portkey to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Portkey + OpenAI Agents SDK FAQ
Common questions about integrating Portkey MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Portkey with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Portkey to OpenAI Agents SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.