How to Use the Addepar MCP in Mastra AI

Build resilient wealth management workflows in Mastra AI. Handle Addepar API rate limits and failures with automatic retries.

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Connect Addepar MCP to Mastra AI

Create your Vinkius account to connect Addepar to Mastra AI 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 Mastra AI

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Route logic based on Addepar MCP Server data

Checking performance thresholds starts with `get_portfolio_analytics` to pull the latest return metrics. Mastra AI takes these numbers and feeds them into conditional workflow branches. If a portfolio drops below a target yield, the engine automatically routes the alert to an advisor. You do not have to write custom polling loops. The agent evaluates the numbers and decides whether to trigger a rebalancing step or simply log the status.

Audit transactions with automatic backoff

Pulling historical trade data means executing `list_transactions` across hundreds of accounts. Financial APIs occasionally timeout during heavy loads. Mastra catches these dropped requests and applies exponential backoff before trying again. The workflow keeps running even if the upstream service hiccups. Your compliance audits finish reliably because the MCP Server handles the network unreliability for you.

Require human approval for account changes

Finding specific client profiles requires running `list_entities` followed by `get_entity_details` to extract the full record. When your agent wants to draft an email based on these private holdings, you can pause the execution. Setting requireToolApproval in your configuration forces the workflow to halt. A human reviews the drafted message and the fetched data before letting the agent proceed.

Setup guide

Set up Addepar MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Addepar tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "addepar-mcp-client",
  servers: {
    "addepar-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Addepar Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Addepar tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Addepar transactions"
);
console.log(result.text);

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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 Mastra AI

Install @mastra/mcp@latest. Instantiate a new MCPClient with your Vinkius URL, then call mcpClient.listTools() and spread them into your agent's tool array.

The framework automatically applies exponential backoff if a request drops. You configure the retry limits directly in your workflow definition.

Mastra auto-detects the transport layer. It will negotiate Streamable HTTP or SSE automatically when it connects to the MCP endpoint.

Create a step that fetches positions, then pass that output to a formatting step. The engine handles passing the state between nodes reliably.

Every request runs through a zero-trust architecture. When your workflow pulls asset allocations, the data passes through a temporary isolate that vanishes the millisecond the connection closes.

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