Intrinio MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Intrinio through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"intrinio": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Intrinio, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Intrinio through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
The Intrinio MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain 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 LangChain via MCP
Follow these steps to integrate the Intrinio MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Intrinio via MCP
Why Use LangChain with the Intrinio MCP Server
LangChain provides unique advantages when paired with Intrinio through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Intrinio MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Intrinio queries for multi-turn workflows
Intrinio + LangChain Use Cases
Practical scenarios where LangChain combined with the Intrinio MCP Server delivers measurable value.
RAG with live data: combine Intrinio tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Intrinio, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Intrinio tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Intrinio tool call, measure latency, and optimize your agent's performance
Intrinio MCP Tools for LangChain (10)
These 10 tools become available when you connect Intrinio to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Intrinio to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersIntrinio + LangChain FAQ
Common questions about integrating Intrinio MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.