How to Use the Ghostfolio (Investment Tracker) MCP in AutoGen

Q: How do I load Ghostfolio (Investment Tracker) into AutoGen?

Use mcpservertools with StreamableHttpServerParams. The McpToolAdapter automatically converts the schema so you can pass the tools directly to your AssistantAgent constructor.

Wire Ghostfolio into AutoGen to build multi-agent financial teams that debate, analyze, and execute your investment strategy.

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Connect Ghostfolio (Investment Tracker) MCP to AutoGen

Create your Vinkius account to connect Ghostfolio (Investment Tracker) to AutoGen 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 AutoGen

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Multi-agent AutoGen portfolio analysis

This MCP Server feeds your raw wealth data directly into Microsoft's conversation framework. You set up a performance agent and a risk management agent. The performance agent calls `get_portfolio_summary` to look for growth, while the risk agent calls `get_portfolio_holdings` to check your asset concentration. They debate the results. One agent pushes for aggressive reallocation based on the numbers, while the other challenges the strategy. You get a consensus-driven decision before anyone touches your money. This is how you build a financial system that actually thinks.

Consensus-driven trade logging

Data entry shouldn't happen blindly. When you need to record a batch of trades, your data-entry agent proposes the changes. A secondary auditor agent reviews the proposed payload, checks it against recent history using `list_activities`, and verifies the logic. Once both agents agree the data is clean, the system executes `create_activity` to log the transaction. If an agent spots a duplicate, it can immediately trigger `delete_activity` to fix the error. You get built-in peer review for every single database write.

Automated account management and pricing

Your agent team can handle the administrative backend of your portfolio. They can run `list_accounts` to audit your current structure, then use `create_account` or `update_account` to organize new asset classes as your strategy evolves. When evaluating a new position, the research agent hits `get_market_data_price` to pull the current ticker valuation. It feeds that price into the group chat. The agents negotiate the entry point based on live market realities, not stale assumptions. You just watch the logs and approve the final plan.

Setup guide

Set up Ghostfolio (Investment Tracker) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Ghostfolio (Investment Tracker) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Ghostfolio (Investment Tracker)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Ghostfolio (Investment Tracker) data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Ghostfolio (Investment Tracker)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Ghostfolio (Investment Tracker) data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Ghostfolio. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Ghostfolio (Investment Tracker) MCP in AutoGen

Use mcp_server_tools with StreamableHttpServerParams. The McpToolAdapter automatically converts the schema so you can pass the tools directly to your AssistantAgent constructor.

Yes. You can equip different agents with specific tools. A researcher might get pricing tools, while an execution agent gets the activity creation tools.

If a tool call fails, the executing agent receives the error string, discusses the failure in the group chat, and attempts a corrected payload based on feedback from other agents.

No. The MCP standard handles all the translation. You just provide the endpoint URL, and AutoGen maps the server's tools into native callable functions.

Your portfolio balances, individual holdings, and buy/sell logs remain strictly within your local AutoGen execution environment. The MCP Server architecture ensures zero-trust data handling, so your sensitive wealth metrics never leave your controlled infrastructure.

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