Massive MCP Server for LlamaIndexGive LlamaIndex 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

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Massive as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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

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python

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 Massive. "
            "You have 1 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Massive?"
    )
    print(response)

asyncio.run(main())

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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.

LlamaIndex agents combine Massive tool responses with indexed documents for comprehensive, grounded answers. Connect 1 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

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 LlamaIndex 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 LlamaIndex

When LlamaIndex 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 LlamaIndex via MCP

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

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 1 tools from Massive

Why Use LlamaIndex with the Massive MCP Server

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

Data-first architecture: LlamaIndex agents combine Massive tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Massive tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Massive, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Massive tools were called, what data was returned, and how it influenced the final answer

Massive + LlamaIndex Use Cases

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

Hybrid search: combine Massive real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Massive 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 Massive for fresh data

Analytical workflows: chain Massive queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Massive in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Massive + LlamaIndex FAQ

Common questions about integrating Massive MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Massive tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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