How to Use the Knoema MCP in OpenAI Agents SDK

Q: Can I cache the Knoema tool definitions in OpenAI Agents SDK?

Yes, set cacheToolsList=True in your server configuration to avoid redundant network roundtrips. This ensures your agent starts up instantly while maintaining access to tools like searchdataseries.

Q: What is the fastest way to get the most recent data points?

Use the getlatestdatasetdata tool. It bypasses historical archives to grab the newest values for your target dataset, which is perfect for real-time market monitoring.

Get global economic indicators directly inside your OpenAI Agents SDK workflows without manually scraping endpoints.

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Connect Knoema MCP to OpenAI Agents SDK

Create your Vinkius account to connect Knoema to OpenAI Agents SDK 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 Knoema with OpenAI Agents SDK

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Target the right data with OpenAI Agents SDK

Stop guessing which dataset contains the inflation or GDP metrics your agent needs. Your OpenAI Agents SDK pipeline uses `search_datasets` through this MCP Server to locate precise dataset IDs instantly, while `list_data_topics` lets the agent map out the exact statistical domains available. Your agent automatically discovers these capabilities at runtime. Using the built-in OpenAI tracing dashboard, you can watch your agent drill down from a broad topic search to a specific dataset without writing custom routing code.

Pull verified time-series data through this MCP Server

Raw numbers require context, and this MCP Server delivers it by exposing `get_data_series` alongside `list_data_units`. Your OpenAI agents can extract deep historical records and instantly verify whether they are looking at percentages, US dollars, or index values. OpenAI's built-in guardrails validate these tool calls before they run. If your agent attempts to query a malformed mnemonic, the pre-execution check catches the error, preventing broken data from corrupting your downstream analytics.

Audit dataset structures programmatically

Before pulling millions of rows, your agent needs to know the layout. This MCP integration lets your system call `get_dataset_metadata` and `list_dataset_regions` to map out geographical coverage and variable definitions before executing heavy data pulls. Because OpenAI Agents SDK supports direct agent handoffs, you can have one agent search the catalog while a specialized analyst agent uses `get_latest_dataset_data` to fetch the actual numbers. This keeps your production workflows modular and fast.

Setup guide

Set up Knoema MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Knoema tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Knoema tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Knoema tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Knoema Agent",
            instructions="You have access to Knoema tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Knoema MCP in OpenAI Agents SDK

Install the SDK via pip and initialize the MCP Server using `MCPServerStreamableHttp` with your Vinkius endpoint. Pass the server instance inside the `mcp_servers` list when instantiating your agent. The agent auto-discovers all ten tools immediately.

Yes, set `cacheToolsList=True` in your server configuration to avoid redundant network roundtrips. This ensures your agent starts up instantly while maintaining access to tools like `search_data_series`.

You should handle rate limits within your agent's execution loop or via Vinkius proxy settings. When tools like `get_data_series` return high-volume economic indicators, the OpenAI dashboard traces the latency so you can optimize query frequency.

Use the `get_latest_dataset_data` tool. It bypasses historical archives to grab the newest values for your target dataset, which is perfect for real-time market monitoring.

Vinkius runs the MCP Server inside a zero-trust, ephemeral V8 Isolate Sandbox. Your queries for global economic statistics and dataset IDs are processed in isolation, and no data is stored or logged permanently on our infrastructure.

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