How to Use the Ghostfolio (Investment Tracker) MCP in OpenAI Agents SDK

Q: How do I connect Ghostfolio (Investment Tracker) to the OpenAI Agents SDK?

Install the package via pip install openai-agents. Then initialize MCPServerStreamableHttp with your endpoint URL and pass it to the mcpservers array in your Agent constructor.

Q: Can my OpenAI Agents SDK system automatically update Ghostfolio (Investment Tracker) accounts?

Yes, your agent can execute updateaccount or createaccount autonomously. The SDK's built-in tracing lets you review every parameter the model passed to the Ghostfolio API.

Q: Does the OpenAI Agents SDK support caching for Ghostfolio (Investment Tracker) tools?

It does. Pass cacheToolsList=True when setting up the server connection. This stops the agent from refetching the nine available investment tools on every single turn.

Q: How do I pull a complete wealth summary into my agent?

Trigger the getportfoliosummary tool. The agent receives the JSON payload containing your performance metrics, which it can then format into a human-readable report.

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Connect Ghostfolio (Investment Tracker) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Ghostfolio (Investment Tracker) 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 Ghostfolio (Investment Tracker) with OpenAI Agents SDK

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Safe trade logging via the Ghostfolio MCP Server

OpenAI's built-in guardrails validate every buy and sell order before it hits your ledger. When your agent calls `create_activity` or `delete_activity`, you can trace the exact execution path in the OpenAI dashboard. This prevents hallucinated trades from corrupting your financial history. You build specialized agents that handle data entry while the SDK ensures they stay within strict operational boundaries.

Handoffs for portfolio analysis

Break your wealth management into distinct agent roles. One agent uses `list_activities` to map historical transactions, while a separate analyst agent takes that context to run `get_portfolio_summary`. The OpenAI Agents SDK handles the MCP routing automatically. You get complex financial breakdowns without writing custom logic to pass context between the data gatherer and the number cruncher.

Real-time market data retrieval

Financial agents need live context to make sense of your holdings. By calling `get_market_data_price`, your Python application pulls the latest ticker valuations directly into the LLM's context window. Combine this with `get_portfolio_holdings` to generate accurate net worth reports. Set `cacheToolsList=True` during initialization so your agent skips the discovery phase and gets straight to pulling prices.

Setup guide

Set up Ghostfolio (Investment Tracker) 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 Ghostfolio (Investment Tracker) tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the package via `pip install openai-agents`. Then initialize `MCPServerStreamableHttp` with your endpoint URL and pass it to the `mcp_servers` array in your Agent constructor.

Yes, your agent can execute `update_account` or `create_account` autonomously. The SDK's built-in tracing lets you review every parameter the model passed to the Ghostfolio API.

It does. Pass `cacheToolsList=True` when setting up the server connection. This stops the agent from refetching the nine available investment tools on every single turn.

Trigger the `get_portfolio_summary` tool. The agent receives the JSON payload containing your performance metrics, which it can then format into a human-readable report.

The server only exposes the specific trade histories and account balances you authorize. OpenAI processes these transaction records during the session, but your core Ghostfolio database remains isolated behind the Vinkius V8 sandbox.

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