How to Use the Addepar MCP in LlamaIndex

Index wealth management data from the Addepar MCP Server into LlamaIndex for grounded RAG applications.

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Connect Addepar MCP to LlamaIndex

Create your Vinkius account to connect Addepar to LlamaIndex 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 LlamaIndex

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Index Addepar MCP Server holdings

RAG usually means parsing PDFs, but financial portfolio queries demand structured truth. You configure LlamaIndex to run `get_position_details` and `get_portfolio_analytics` across your client base. The framework ingests these raw holdings and performance metrics straight into your vector store via MCP tools. When an advisor asks which clients hold too much tech stock, the engine queries the index instead of guessing. You get answers grounded in actual portfolio weights rather than outdated summaries.

Build a searchable entity knowledge base

Tracking down account structures often requires digging through multiple dashboards. Using `list_entities` and `get_entity_details`, your application pulls the full hierarchy of family office accounts into a unified RAG setup. The tool spec converts the API response into documents LlamaIndex understands. An advisor just types a natural language question about a specific trust, and the system retrieves the exact ownership details from the index.

Query historical cash flows semantically

Finding a specific wire transfer or block trade is tedious work. By feeding `list_transactions` into your data ingestion pipeline, you make the entire financial history searchable via embeddings. Users ask abstract questions about past liquidity events. The semantic search maps their intent to the indexed transaction records, pulling up the exact dates and amounts without needing a rigid SQL query.

Setup guide

Set up Addepar MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Addepar MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Addepar tools.",
)
response = await agent.run("List recent Addepar data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Addepar. 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 Addepar MCP in LlamaIndex

Install the llama-index-tools-mcp package and instantiate a BasicMCPClient. Wrap it with McpToolSpec and call to_tool_list_async() to feed the operations into your FunctionAgent.

You control the inputs entirely. Pass specific client IDs to the ingestion script so the vector store only contains the portfolios you actually want to query.

The framework handles the translation from structured JSON to indexable documents. You configure how the numerical data gets represented for optimal retrieval.

Dashboards force you to click through pre-defined views. A RAG setup lets you ask ad-hoc questions across thousands of accounts and get immediate, mathematically grounded answers.

Vinkius routes your traffic through a zero-trust architecture. We never log the high-net-worth names, account balances, or transaction amounts you index. Your keys stay secure, and the sandbox vanishes after execution.

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