Polygon.io MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Get Aggregates, Get Company, List Dividends, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Polygon.io as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this MCP Server for LlamaIndex
The Polygon.io MCP Server for LlamaIndex is a standout in the Money Moves category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Polygon.io. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Polygon.io?"
)
print(response)
asyncio.run(main())
* 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 Polygon.io MCP Server
Connect your Polygon.io account to any AI agent to retrieve institutional-grade financial data through natural language. Monitor market trends, analyze historical performance, and inspect company fundamentals without leaving your chat interface.
LlamaIndex agents combine Polygon.io tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Market Aggregates — Fetch OHLCV (Open, High, Low, Close, Volume) bars for any ticker over custom timeframes (minute, hour, day)
- Real-Time & Historical Trades — Access tick-by-tick trade data to analyze precise market movements and execution prices
- Company Fundamentals — Retrieve detailed metadata for stock tickers, including industry classifications and company descriptions
- Dividend Tracking — List historical cash dividend distributions, frequencies, and ex-dividend dates for specific assets
- Ticker Discovery — Query the full list of supported tickers and financial instruments available on the platform
The Polygon.io MCP Server exposes 6 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 6 Polygon.io tools available for LlamaIndex
When LlamaIndex connects to Polygon.io through Vinkius, your AI agent gets direct access to every tool listed below — spanning stock-market, trading-data, financial-analysis, 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.
Get aggregates on Polygon.io
Get aggregate bars for a stock
Get company on Polygon.io
Get company details for a ticker
List dividends on Polygon.io
List historical dividends
List quotes on Polygon.io
Get tick-by-tick quotes (NBBO)
List tickers on Polygon.io
io. Query all supported tickers
List trades on Polygon.io
Get tick-by-tick trades
Connect Polygon.io to LlamaIndex via MCP
Follow these steps to wire Polygon.io into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Polygon.io MCP Server
LlamaIndex provides unique advantages when paired with Polygon.io through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Polygon.io tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Polygon.io tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Polygon.io, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Polygon.io tools were called, what data was returned, and how it influenced the final answer
Polygon.io + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Polygon.io MCP Server delivers measurable value.
Hybrid search: combine Polygon.io real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Polygon.io 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 Polygon.io for fresh data
Analytical workflows: chain Polygon.io queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Polygon.io in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Polygon.io immediately.
"Get company details and industry for ticker NVDA."
"Show me the historical dividends for MSFT."
"Get daily aggregate bars for TSLA from 2024-01-01 to 2024-01-10."
Troubleshooting Polygon.io MCP Server with LlamaIndex
Common issues when connecting Polygon.io to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPolygon.io + LlamaIndex FAQ
Common questions about integrating Polygon.io MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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