Addepar MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Addepar as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Addepar. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in Addepar?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Addepar MCP Server
Connect your Addepar account to your AI agent to unlock enterprise-grade investment intelligence and reporting. From auditing portfolio performance to tracking granular transactions and managing complex ownership structures, your agent handles wealth management data through natural conversation.
LlamaIndex agents combine Addepar tool responses with indexed documents for comprehensive, grounded answers. Connect 5 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Portfolio Intelligence — Retrieve detailed performance and analytics for your clients and entity groups
- Entity Management — List and audit clients, accounts, and investment groups to maintain organizational clarity
- Position Tracking — View real-time holdings and ownership details across your entire investment landscape
- Transaction Auditing — Retrieve and analyze financial transaction logs to ensure accuracy and transparency
- Metadata Insights — Access deep technical metadata for any entity or account directly from your chat interface
The Addepar MCP Server exposes 5 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Addepar to LlamaIndex via MCP
Follow these steps to integrate the Addepar MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 5 tools from Addepar
Why Use LlamaIndex with the Addepar MCP Server
LlamaIndex provides unique advantages when paired with Addepar through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Addepar tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Addepar tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Addepar, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Addepar tools were called, what data was returned, and how it influenced the final answer
Addepar + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Addepar MCP Server delivers measurable value.
Hybrid search: combine Addepar real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Addepar to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Addepar for fresh data
Analytical workflows: chain Addepar queries with LlamaIndex's data connectors to build multi-source analytical reports
Addepar MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect Addepar to LlamaIndex via MCP:
get_entity_details
Get details for an entity
get_portfolio_analytics
Get portfolio performance data
get_position_details
View portfolio holdings
list_entities
List clients and accounts
list_transactions
List financial transactions
Example Prompts for Addepar in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Addepar immediately.
"List all active client entities in my Addepar account."
"Show me the performance for 'The Miller Family Office' for the last quarter."
"List the latest 10 transactions for account ID ACCT-123."
Troubleshooting Addepar MCP Server with LlamaIndex
Common issues when connecting Addepar to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAddepar + LlamaIndex FAQ
Common questions about integrating Addepar MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Addepar with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Addepar to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.