How to Use the Addepar MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK setup handle complex entity hierarchies from Addepar?

Yes, the agent calls listentities to map out client relationships and households. It then uses those IDs to query specific account details using getentitydetails in a single run.

Feed live portfolio data directly to your OpenAI Agents SDK pipelines to run automated financial reporting.

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Connect Addepar MCP to OpenAI Agents SDK

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

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Live Portfolio Audits in OpenAI Agents SDK

The `get_portfolio_analytics` tool pulls raw performance metrics directly into your OpenAI Agents SDK runtime. Your agent reads these metrics to calculate net-of-fee returns or flag underperforming asset classes during a run. The agent triggers this call during its execution loop, feeding the structured performance numbers straight into your custom validation guards. This keeps your automated wealth reports grounded in real financial facts without manual exports.

Automated Transaction Auditing with MCP

The `list_transactions` tool exposes historical trade data to your OpenAI Agents SDK setup. Your agent uses this MCP tool to parse transactions, spot anomalies, or verify trade execution dates. Because this MCP Server integrates directly with your agentic workflows, you can configure a supervisor agent to hand off transaction discrepancies to a human reviewer. This setup keeps your compliance pipeline running without human bottlenecking.

Zero-Config Entity Resolution

The `list_entities` tool pulls your entire directory of clients, households, and accounts directly into the OpenAI Agents SDK workspace. Your agent uses this directory to map complex ownership structures and resolve entity IDs instantly. Once the agent identifies the correct target, it chains this data into `get_entity_details` to retrieve specific account profiles. The SDK manages this transition safely, passing structured context between specialized agents without losing track of the client identity.

Setup guide

Set up Addepar 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 Addepar tools at runtime.
3

Create your Agent

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

Run the agent

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

You register the Addepar MCP Server directly in your server list when initializing the Agent class. The SDK automatically discovers the `get_portfolio_analytics` tool and exposes it to your agents.

Yes, the agent calls `list_entities` to map out client relationships and households. It then uses those IDs to query specific account details using `get_entity_details` in a single run.

The SDK uses built-in guardrails to validate arguments before executing tools like `list_transactions`. You can intercept and inspect the transaction parameters in your Python code before the actual API call executes.

Yes, this MCP Server runs inside an asynchronous context manager. It handles concurrent requests from multiple OpenAI Agents SDK threads without blocking your main event loop.

Your transaction records and portfolio metrics remain inside the secure V8 sandbox. The server processes all data ephemerally, ensuring no financial information is stored or cached on our platform.

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