How to Use the Zippopotam.us MCP in LangChain

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Connect Zippopotam.us MCP to LangChain

Create your Vinkius account to connect Zippopotam.us 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.

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Setup Zippopotam.us with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-step location lookup.

Need to find a city's zip code, but you don't know the right input? You call `lookup_nearby_city` first. The agent uses that result—the list of possible zips—and passes it into your next step for filtering or processing. This lets you build multi-step pipelines where the agent decides WHICH tool to call and in WHAT ORDER based on intermediate results, making complex data retrieval reliable.

Coordinate verification.

The `lookup_postcode` tool instantly resolves a zip code into precise geographical coordinates. This means your system doesn't just know the area; it knows exactly where that location is on a map. You can chain this coordinate data to other services—like mapping APIs—to verify site presence or calculate travel distance, all within one LangChain sequence.

Data context chaining.

You're processing records that need geo-data attached. Start by using `lookup_nearby_city` to get the necessary zip code range for a known metropolitan area. This output then becomes a filter applied to your internal database queries. This approach ensures every piece of data you move through the chain is geographically accurate, eliminating guesswork and keeping your workflow tight.

Setup guide

Set up Zippopotam.us 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 Zippopotam.us 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({
    "zippopotamus-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 Zippopotam.us 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 Zippopotam.us. 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.

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Common questions about Zippopotam.us MCP in LangChain

Your LangChain agent gains a dedicated toolset for geography. It handles the complexity of knowing whether to use `lookup_nearby_city` or `lookup_postcode` based on what you provide it. This makes your data pipelines much more robust right out of the box.

Yes, because LangChain supports session management, you can keep state between tool calls. If a user needs to look up multiple related postal codes over time, the agent retains that context for efficiency.

The server deals with geographical identifiers: zip codes, city names, and associated coordinates. When running a chain, you are passing these specific location types through the system for resolution.

Absolutely. You can build a loop or an agent that iterates over several city names, calling `lookup_nearby_city` sequentially. The resulting list of zip codes becomes the aggregate output of your chain.

This MCP Server touches geographical identifiers, specifically city names and postal codes. All interactions are handled through an endpoint token, keeping the actual underlying data secure.

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