How to Use the Aragón Open Data MCP in LlamaIndex

Q: How do I query Aragón Open Data with LlamaIndex?

Use BasicMCPClient to connect to the Vinkius endpoint. Convert the available tools with McpToolSpec and pass them to your FunctionAgent.

Q: How do I find specific dataset themes?

Your agent calls listgroups to retrieve all available categories. It then filters the RAG retrieval step to only include documents from those specific themes.

Index public records from the Aragón government directly into your LlamaIndex RAG applications.

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Connect Aragón Open Data MCP to LlamaIndex

Create your Vinkius account to connect Aragón Open Data to LlamaIndex 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 LlamaIndex

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Index regional datasets

The `list_datasets` tool pulls the entire catalog of available packages through this MCP integration. Your RAG pipeline ingests these metadata records and converts them into searchable embeddings. When a user asks about regional statistics, the engine doesn't hallucinate. It runs semantic search against the indexed catalog to find the exact source.

Embed SPARQL results

Running `query_sparql` fetches specific entities from the ELI or DataCube ontologies. You capture this structured output and feed it straight into your document store. This bridges the gap between raw semantic web data and natural language queries. Users ask plain English questions and get answers grounded in official government triples.

Build an Aragón Open Data MCP Server index

Calling `preview_data` retrieves up to 1000 records from any specific view. LlamaIndex chunks these rows and creates a localized vector index for deep querying. Combine this with `get_organization` to attach publisher metadata to every chunk. Now your queries filter results by specific government departments before running the similarity search.

Setup guide

Set up Aragón Open Data MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Aragón Open Data MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Aragón Open Data tools.",
)
response = await agent.run("List recent Aragón Open Data data")

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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 LlamaIndex

Use `BasicMCPClient` to connect to the Vinkius endpoint. Convert the available tools with `McpToolSpec` and pass them to your `FunctionAgent`.

The framework easily handles the JSON arrays returned by the preview tools. Developers define the chunk size and overlap just like any other document loader.

You can route the fetched government records into any supported database. The MCP architecture pulls the data, structures it, and writes it to your chosen storage backend.

Your agent calls `list_groups` to retrieve all available categories. It then filters the RAG retrieval step to only include documents from those specific themes.

We process your dataset searches and SPARQL queries in memory. Your MCP Server interactions with the CKAN catalog disappear as soon as the connection closes thanks to our zero-trust model.

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