Massive MCP Server for AutoGenGive AutoGen instant access to 1 tools to List Dividends

Name: Massive
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Massive as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

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The Massive MCP Server for AutoGen is a standout in the Data Analytics category — giving your AI agent 1 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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+ other MCP clients

python

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="massive_agent",
            tools=tools,
            system_message=(
                "You help users with Massive. "
                "1 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

Fully ManagedVinkius Servers

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Massive MCP Server

Connect to Massive to retrieve comprehensive historical dividend data for thousands of tickers. Empower your AI agent to perform deep financial analysis and equity research through natural conversation.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Massive tools. Connect 1 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

What you can do

Historical Dividends — Fetch full records of cash distributions for any supported stock ticker from the Massive API.
Granular Filtering — Filter results by ex-dividend date, frequency (annual, quarterly), or specific distribution types.
Distribution Types — Identify recurring, special, supplemental, or irregular dividends to understand company payout patterns.
Data Analysis — Sort and limit results (up to 5000 records) to build precise financial models or investment reports.

The Massive MCP Server exposes 1 tools through the Vinkius. Connect it to AutoGen in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 1 Massive tools available for AutoGen

When AutoGen connects to Massive through Vinkius, your AI agent gets direct access to every tool listed below — spanning dividends, stock-market, financial-data, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

list

List dividends on Massive

Retrieve historical cash dividends for a ticker

Connect Massive to AutoGen via MCP

Follow these steps to wire Massive into AutoGen. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 1 tools from Massive automatically

Why Use AutoGen with the Massive MCP Server

AutoGen provides unique advantages when paired with Massive through the Model Context Protocol.

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Massive tools to solve complex tasks

Role-based architecture lets you assign Massive tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Massive tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Massive tool responses in an isolated environment

Massive + AutoGen Use Cases

Practical scenarios where AutoGen combined with the Massive MCP Server delivers measurable value.

Collaborative analysis: one agent queries Massive while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Massive, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Massive data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Massive responses in a sandboxed execution environment

Example Prompts for Massive in AutoGen

Ready-to-use prompts you can give your AutoGen agent to start working with Massive immediately.

"List historical dividends for ticker 'AAPL'."

"Show me special dividends for 'MSFT' sorted by date."

"Find all dividends for 'KO' with a frequency of 4."

Troubleshooting Massive MCP Server with AutoGen

Common issues when connecting Massive to AutoGen through Vinkius, and how to resolve them.

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Massive + AutoGen FAQ

Common questions about integrating Massive MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Massive tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.