Intrinio MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Intrinio 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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Vinkius supports streamable HTTP and SSE.
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 Intrinio. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Intrinio?"
)
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 Intrinio MCP Server
Empower your AI agents with Intrinio's comprehensive financial data. This MCP server allows you to retrieve real-time and historical stock prices, access financial statements, search for companies, and track earnings releases and IPO calendars directly through the Intrinio API. Ideal for financial analysis, portfolio monitoring, and market research.
LlamaIndex agents combine Intrinio tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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.
The Intrinio MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Intrinio to LlamaIndex via MCP
Follow these steps to integrate the Intrinio MCP Server with LlamaIndex.
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 10 tools from Intrinio
Why Use LlamaIndex with the Intrinio MCP Server
LlamaIndex provides unique advantages when paired with Intrinio through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Intrinio tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Intrinio tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Intrinio, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Intrinio tools were called, what data was returned, and how it influenced the final answer
Intrinio + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Intrinio MCP Server delivers measurable value.
Hybrid search: combine Intrinio real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Intrinio 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 Intrinio for fresh data
Analytical workflows: chain Intrinio queries with LlamaIndex's data connectors to build multi-source analytical reports
Intrinio MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Intrinio to LlamaIndex via MCP:
get_company
g., "AAPL") or ID. Returns headquarters address, employee counts, and business descriptions. Useful for providing a profile overview of a company. Retrieves details for a specific company
get_earnings_releases
Essential for tracking reporting seasons and anticipating market volatility for specific tickers. Lists upcoming and past earnings releases
get_financials
Returns line items and values. Essential for fundamental financial analysis and performance vetting. Retrieves financial statements for a company
get_ipo_calendar
Useful for identifying new market entrants and investment opportunities. Retrieves the IPO calendar
get_security
Returns exchange info, security type, and identifiers. Use this to distinguish between different types of instruments traded under similar names. Retrieves details for a specific security
get_stock_prices
Use this to analyze market performance and price trends over time. Retrieves historical stock prices for a security
list_companies
Returns company names, tickers, and internal IDs. Use this to discover tickers before querying specific stock prices or financial statements. Lists all companies covered by Intrinio
list_indices
g., S&P 500, Dow Jones) tracked by Intrinio. Use this to identify index identifiers before querying index performance data. Lists stock market indices
list_news
Useful for monitoring market-moving events and recent announcements from public companies. Lists latest financial news
search_companies
Use this when the user provided a partial company name and you need to locate the correct ticker or ID. Searches for companies by name or ticker
Example Prompts for Intrinio in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Intrinio immediately.
"List financial statements for Apple (AAPL)."
"Get the latest stock price for Microsoft."
"Search for companies in the 'Software' industry."
Troubleshooting Intrinio MCP Server with LlamaIndex
Common issues when connecting Intrinio to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpIntrinio + LlamaIndex FAQ
Common questions about integrating Intrinio 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?
Connect Intrinio with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Intrinio to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.