How to Use the Massive MCP in CrewAI

Q: How does Massive support CrewAI autonomous agents?

Massive provides the listdividends tool as a reliable data source. Your CrewAI agents call this tool to gather historical payout records without needing human oversight.

Q: Can CrewAI agents use Massive for automated portfolio rebalancing?

They can. By using listdividends to pull payout history, your CrewAI agents can identify dividend-paying stocks and execute rebalancing tasks automatically.

Q: How do I manage tool access in a CrewAI crew?

Use the toolfilter parameter in your agent setup. This ensures your CrewAI agents only see the tools they need for their specific role.

Deploy autonomous research crews with CrewAI. Massive provides the historical dividend data your agents need to make decisions.

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Connect Massive MCP to CrewAI

Create your Vinkius account to connect Massive 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 Massive with CrewAI

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Autonomous dividend research with CrewAI

Your research agents use `list_dividends` to build comprehensive portfolios without manual input. They query the history, compare payouts, and pass the results to your analyst agents. This turns your crew into a self-sufficient financial engine. Agents handle the data gathering while you focus on the strategy.

Specialized agent collaboration

CrewAI agents share memory, allowing one agent to fetch dividend data and another to interpret the yield trends. The `list_dividends` tool serves as the shared data source. Your crew stays synchronized throughout the entire analysis process. It prevents redundant queries and keeps the data consistent across all agents.

Scalable framework integration

Massive connects to your CrewAI setup using standard HTTP or SSE transports. It works in sequential or hierarchical team structures without complex configuration. Just drop the server URL into your agent config. The tools are ready for your crew to start working immediately.

Setup guide

Set up Massive 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 Massive tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Massive 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="Massive Analyst",
        goal="Access and analyze Massive data via MCP.",
        backstory="Expert analyst with direct Massive access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Massive 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 →

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Common questions about Massive MCP in CrewAI

Massive provides the `list_dividends` tool as a reliable data source. Your CrewAI agents call this tool to gather historical payout records without needing human oversight.

It is. Whether you run sequential or hierarchical crews, the tool is available to every agent in the team via the shared MCP connection.

They can. By using `list_dividends` to pull payout history, your CrewAI agents can identify dividend-paying stocks and execute rebalancing tasks automatically.

Use the tool_filter parameter in your agent setup. This ensures your CrewAI agents only see the tools they need for their specific role.

Massive uses zero-trust security. Your ticker search queries are never cached or shared, ensuring that each CrewAI session remains entirely isolated and confidential.

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