How to Use the EOD Historical Data MCP in LangChain

Q: How do I hook up the EOD Historical Data MCP Server to my LangChain agents?

Install the langchain-mcp-adapters package and initialize the client using your Vinkius endpoint. Pass the tools directly to your agent runner to fetch historical prices and fundamental metrics.

Q: Can LangChain run sequential analysis using EOD Historical Data tools?

Yes. Your LangChain agent calls getscreener to find stocks and then passes those symbols to gettechnicalindicators in a single run. The output of one tool serves as the direct input for the next.

Q: Does this server support multi-asset pipelines in LangChain?

Yes. You chain getcommodities and getustyieldrates together to let your LangChain agent compare macroeconomic trends against equity performance in real time.

Get raw financial metrics directly into your LangChain pipelines without writing custom API wrappers.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect EOD Historical Data MCP to LangChain

Create your Vinkius account to connect EOD Historical Data to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup EOD Historical Data with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain market data directly into agent decisions

The `get_eod_historical` tool feeds raw pricing data directly into your LangChain decision chains. Your agent runs the numbers, decides if a stock fits your criteria, and immediately passes the output to the next step using this MCP Server. You don't write glue code to move stock prices between analytical steps. If the initial check passes, the agent triggers `get_fundamentals` to pull balance sheets. LangSmith traces the entire multi-step run, showing you the exact inputs, outputs, and latency of each financial tool execution.

Build multi-step financial reasoning pipelines

The `get_screener` tool filters down thousands of equities based on your custom parameters. LangChain coordinates this initial filter with immediate follow-up actions using the MCP framework. The agent takes the filtered ticker list and feeds it to other tools in the chain. From there, the chain automatically triggers `get_calendar_earnings` to check for upcoming volatility drivers. This setup handles the entire sequence of API calls in a single, observable execution trace.

Track sentiment trends with LangChain observability

The `get_sentiments` tool pulls current market mood metrics directly into your LangChain active memory. Your agent processes these scores alongside raw news feeds to build a real-time market overview. You combine this with `get_news_word_weights` to see what terms drive market movement. LangChain tracks every token spent and tool called, letting you debug complex financial reasoning steps instantly.

Setup guide

Set up EOD Historical Data MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes EOD Historical Data tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "eod-historical-data-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent EOD Historical Data transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by EOD Historical Data. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect EOD Historical Data now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about EOD Historical Data MCP in LangChain

Install the `langchain-mcp-adapters` package and initialize the client using your Vinkius endpoint. Pass the tools directly to your agent runner to fetch historical prices and fundamental metrics.

Yes. Your LangChain agent calls `get_screener` to find stocks and then passes those symbols to `get_technical_indicators` in a single run. The output of one tool serves as the direct input for the next.

LangChain executes tool calls as standard python functions, meaning you wrap your agent execution or the MCP client in standard rate-limiting middleware. You also monitor your API usage directly using `get_user`.

Yes. You chain `get_commodities` and `get_ust_yield_rates` together to let your LangChain agent compare macroeconomic trends against equity performance in real time.

Your API key and financial queries—like specific ticker searches or fundamental lookups—run through Vinkius's isolated MCP sandbox. No raw financial data or proprietary search symbols ever sit on Vinkius servers or leak to third parties.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript