How to Use the Massive MCP in OpenAI Agents SDK

Q: Does Massive support parallel tool calls in OpenAI Agents SDK?

The agent can trigger multiple listdividends calls simultaneously when analyzing a portfolio of tickers. This parallel execution cuts down the total analysis time for large watchlists.

Feed historical dividend records directly into your OpenAI Agents SDK pipelines using this MCP Server.

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Connect Massive MCP to OpenAI Agents SDK

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

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Automate yield analysis using OpenAI Agents SDK

Your OpenAI Agents SDK agents run the `list_dividends` tool to fetch raw distribution records for any ticker you pass them. This removes the need to write custom web scrapers or manage flaky financial API integrations in your OpenAI Agents SDK pipeline. The OpenAI Agents SDK agent handles the raw JSON payload natively, parsing payment dates and cash amounts instantly. You get clean historical yields mapped directly into your OpenAI Agents SDK decision-making loop.

Enforce safety guardrails on financial lookups

The `list_dividends` tool integrates with the built-in validation layers of this MCP Server to prevent prompt injection from altering ticker queries inside OpenAI Agents SDK. When an agent requests dividend histories, the server checks the input format before hitting the database. This setup ensures your production OpenAI Agents SDK agents never execute malformed financial queries. You get clean, predictable data streams without worrying about rogue agent behavior during market analysis.

Trace dividend data flow in your OpenAI dashboard

Every call to the `list_dividends` tool shows up in your OpenAI Agents SDK tracing dashboard with complete input and output payloads. You see the exact timestamp and ticker parameter your agent sent to the MCP Server. Debugging failed financial runs in OpenAI Agents SDK takes seconds instead of hours of log digging. You monitor the exact cost and latency of every single dividend lookup your agent system executes.

Setup guide

Set up Massive 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 Massive tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Massive 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="Massive Agent",
            instructions="You have access to Massive 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 Massive MCP in OpenAI Agents SDK

Install the package, define the HTTP server parameters with your endpoint URL, and pass the server instance to your agent constructor. The SDK automatically discovers the `list_dividends` tool.

Yes, the SDK manages the connection stream while this server handles the upstream financial data limits. Your agent will back off automatically if it requests too many dividend histories at once.

The agent can trigger multiple `list_dividends` calls simultaneously when analyzing a portfolio of tickers. This parallel execution cuts down the total analysis time for large watchlists.

You can configure guardrails in your agent code to inspect the ticker argument before it reaches the MCP Server. This prevents the agent from querying unapproved assets.

We do not store the stock tickers your agents query, nor do we log the historical dividend data returned. All requests pass through an ephemeral, zero-trust sandbox that wipes your query history instantly.

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