How to Use the Aragón Open Data MCP in LangChain

Q: How do I connect the Aragón Open Data MCP Server to LangChain?

Install langchain-mcp-adapters. Pass the Vinkius endpoint to MultiServerMCPClient and hand the tools over to your ReAct agent.

Q: Can LangChain agents read Aragón Open Data schemas?

Yes, agents call showcolumns to inspect view structures first. They use this information to understand the data before summarizing it.

Fetch Aragón government datasets directly inside your LangChain agents.

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Connect Aragón Open Data MCP to LangChain

Create your Vinkius account to connect Aragón Open 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.

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Setup Aragón Open Data with LangChain

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ChatGPT

Claude

Perplexity

Query SPARQL via LangChain

The `query_sparql` tool lets your agent execute raw queries against ontologies like EI2A and Aragopedia. You build a chain that takes a user question, writes the syntax, and pulls the exact records from the government database. ReAct agents handle the iteration natively. If a query fails, the system reads the error, corrects the syntax, and tries again without human intervention.

Search and preview datasets

Calling `search_datasets` returns matching packages from the CKAN catalog. Your pipeline can then pass those results to `preview_data` to grab the first 1000 records of a specific view. This setup feeds real data into your prompt context. Developers don't guess what the columns mean because `show_columns` exposes the exact schema before the agent writes its summary.

Track catalog updates

The `newest_datasets` tool pulls the latest published packages from the regional government via this MCP Server. We set up scheduled runs that check for fresh uploads and pipe the metadata into a vector store. Connecting `get_organization` reveals which department published the files. LangSmith dashboards track exactly how many tokens the agent spent pulling these daily reports.

Setup guide

Set up Aragón Open 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 Aragón Open 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({
    "aragon-open-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 Aragón Open Data 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 Aragón Open 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.

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Common questions about Aragón Open Data MCP in LangChain

Install `langchain-mcp-adapters`. Pass the Vinkius endpoint to `MultiServerMCPClient` and hand the tools over to your ReAct agent.

Yes, agents call `show_columns` to inspect view structures first. They use this information to understand the data before summarizing it.

You get structured JSON back from the MCP Server. The framework parses this automatically so downstream tasks can read specific fields.

Your agent chains multiple calls together natively. It might search for a topic, find the dataset ID, and then fetch the actual records in one go.

Your MCP session passes through a V8 Isolate Sandbox. Vinkius drops the ephemeral container the moment your session ends, leaving zero trace of your public data research.

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