How to Use the ShowAPI / 易源数据 MCP in LangChain

Build multi-step agents and complex pipelines with LangChain.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect ShowAPI / 易源数据 MCP to LangChain

Create your Vinkius account to connect ShowAPI / 易源数据 to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup ShowAPI / 易源数据 with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Multi-Step Data Aggregation for LangChain

Need a full picture of a location? Start by getting the geographical data using `get_ip_lookup`. Then, chain that output into calling `get_weather` to get current conditions. You can follow that up by checking historical events via `get_history_today`. This pipeline lets your agent build complex reasoning paths. It’s perfect for defining multi-step workflows where the result of one API call dictates the next tool invocation.

Advanced Language Processing with MCP Server

The `translate_text` tool is a key player here. Your agent can first use `get_english_dictionary` to confirm proper terminology, then translate that text into another language. You can even layer in context by retrieving information from `get_idiom_info` for cultural nuance. This setup lets you build sophisticated content generation agents. They don't just translate; they adapt the meaning based on linguistic rules and idioms.

Financial Data Analysis Chain

When building a financial agent, your workflow needs to combine several data points. Use `get_stock_info` to check market prices. Next, chain that with `get_currency_rates` to see the real-world value of those assets in different regions. This structure lets you create full economic analysis chains within LangChain. It’s more powerful than just calling two tools separately; it links them into a single decision point.

Setup guide

Set up ShowAPI / 易源数据 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 ShowAPI / 易源数据 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({
    "showapi-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 ShowAPI / 易源数据 transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ShowAPI / 易源数据. 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

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Common questions about ShowAPI / 易源数据 MCP in LangChain

You pass the available tool definitions from ShowAPI / 易源数据 directly to your agent constructor. The resulting agent knows which tools it has access to, letting it plan multi-step calls automatically.

Absolutely. Since it’s a chain, the output of any tool—say, weather coordinates—becomes the input for the next tool call, like checking local historical events.

The MCP Server provides diverse content. You can chain together everything from `get_stock_info` and currency rates to general knowledge like quotes or jokes, making a rich dataset for the agent.

Yes. The setup supports full observability via tracing tools. You can see exactly which tool was called, what inputs it took, and what output it generated at every step.

The server handles various types of API data, including geographical IP lookup information. Always ensure your agent's context is managed correctly when processing user input.

Use it with your favorite AI tools

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

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