How to Use the Addepar MCP in CrewAI

Q: How do I configure the Addepar MCP Server in CrewAI?

Install crewaitools via pip. You can pass the Vinkius URL directly to your agent using the mcps array parameter, and the framework handles the rest.

Q: Can I restrict Addepar tools to a single CrewAI agent?

Use MCPServerHTTP from crewai.mcp and apply a toolfilter. This ensures your reporting agent cannot accidentally execute transaction queries.

Deploy specialized autonomous agents in CrewAI to monitor Addepar portfolios. Build a financial research team that works 24/7.

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Connect Addepar MCP to CrewAI

Create your Vinkius account to connect Addepar to CrewAI 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 CrewAI

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Assign Addepar accounts to specific agents

Your research agent uses `list_entities` to map out the client hierarchy and `get_entity_details` to build a profile of their risk tolerance. CrewAI lets you restrict these specific read operations to a dedicated analyst role. The analyst gathers the context and saves it to the shared memory pool. Other agents in the crew can access this context without needing to hit the API themselves.

Analyze performance with the Addepar MCP Server

Extracting yield data happens when your quantitative agent calls `get_portfolio_analytics` to grab the raw numbers. In a hierarchical setup, a manager agent reviews these metrics and decides if the portfolio needs attention. The manager can then delegate a task to a reporting agent to draft a client update. The entire process runs autonomously based on the rules you defined.

Monitor asset drift continuously

Tracking allocation shifts requires a monitor agent constantly polling `get_position_details` to watch specific holdings. If Apple stock grows to exceed ten percent of a portfolio, the monitor flags the violation. A moderator agent catches this flag and logs an alert for the human wealth manager. You build a self-operating compliance team using this MCP integration in just a few lines of Python.

Setup guide

Set up Addepar MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Addepar tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Addepar Analyst",
    goal="Access and analyze Addepar data via MCP.",
    backstory="Expert analyst with direct Addepar access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Addepar transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Addepar Analyst",
        goal="Access and analyze Addepar data via MCP.",
        backstory="Expert analyst with direct Addepar access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Addepar transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 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 CrewAI

Install crewai[tools] via pip. You can pass the Vinkius URL directly to your agent using the mcps array parameter, and the framework handles the rest.

Use MCPServerHTTP from crewai.mcp and apply a tool_filter. This ensures your reporting agent cannot accidentally execute transaction queries.

The MCP protocol supports standard input/output, server-sent events, and Streamable HTTP. Vinkius endpoints work perfectly with the HTTP options.

Agents store fetched metrics in the crew's shared memory. Once the analyst pulls the numbers, the writer agent reads them from memory to draft the email.

Vinkius manages authentication via single-use endpoint tokens. When your crew requests trade records, the operation executes inside an isolated sandbox that gets destroyed immediately after returning the JSON.

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