How to Use the Massive MCP in LlamaIndex

Index historical dividend payouts directly into your LlamaIndex knowledge base.

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Connect Massive MCP to LlamaIndex

Create your Vinkius account to connect Massive to LlamaIndex 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 LlamaIndex

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Ground RAG apps with Massive MCP Server data

Running `list_dividends` through LlamaIndex turns raw financial payouts into searchable vectors. Your application fetches the cash dividend history for a specific stock and immediately indexes those facts alongside your internal research PDFs. The agent stops guessing about historical yields because the actual numbers live inside the vector store. Users query the index later to compare past performance across different sectors. The system retrieves the exact payment dates and amounts instead of hallucinating a generic answer. This approach bridges the gap between static documents and live API endpoints.

Build queryable payout histories

The `list_dividends` tool feeds structured distribution records straight into your LlamaIndex data pipeline. You pass the resulting JSON into a document node, and the framework automatically chunks the historical dates and amounts for embedding. Your users then ask natural language questions about specific quarters. Semantic search works better when it has hard numbers to reference. Embedding this dividend data means a query about a specific quarter pulls the exact figures you fetched earlier. Developers build a financial oracle that actually knows what happened.

Combine live distributions with static docs

Triggering `list_dividends` lets your LlamaIndex agent cross-reference real-time API data against your stored company filings. It pulls the factual cash payouts for a ticker and compares them to the dividend policies outlined in the 10-K you already indexed. Analysts spot inconsistencies instantly. This workflow transforms basic document retrieval into active financial analysis. The agent uses the MCP server to grab the missing quantitative pieces that your PDFs lack. You get answers backed by both official statements and actual distribution math.

Setup guide

Set up Massive MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Massive MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Massive tools.",
)
response = await agent.run("List recent Massive data")

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Common questions about Massive MCP in LlamaIndex

Install llama-index-tools-mcp and instantiate a BasicMCPClient. You wrap it with McpToolSpec and pass the async tool list to your FunctionAgent.

Yes. Your agent fetches the dividend records and routes that output directly into your document ingestion pipeline for semantic search.

It does. You restrict your agent to only access the dividend endpoint by passing the specific tool name to the filter list during setup.

The schema tells the agent exactly what fields exist. It knows it receives dates, amounts, and record details without any extra prompting from you.

The server only sees the stock symbols you request to pull cash amounts and payment dates. Vinkius operates a zero-trust MCP architecture, so your queries hit the endpoint and evaporate immediately without hitting a database log.

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