How to Use the Aragón Open Data MCP in Mastra AI

Q: How do I connect the MCP server to Mastra AI for Aragón Open Data?

Install @mastra/mcp@latest. Instantiate the client with new MCPClient() and pass your Vinkius endpoint URL. Spread the tools from listTools() into your agent configuration.

Q: How do I run SPARQL queries against Aragón Open Data in Mastra AI?

Pass the querysparql tool to an agent step. The agent will formulate the query, execute it, and pass the resulting JSON array to the next step in your workflow.

Q: Does this MCP Server support human-in-the-loop approval?

You can enable requireToolApproval for any tool. This is useful if you want a human to review the SPARQL query generated by the agent before it executes.

Build reliable workflows that query and process civic information from the Government of Aragón using Mastra AI.

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Connect Aragón Open Data MCP to Mastra AI

Create your Vinkius account to connect Aragón Open Data to Mastra AI 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 Mastra AI

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Automate Audits via MCP Server

The `list_organizations` tool lets your agent interact with the regional public data catalog. A Mastra AI workflow can trigger this to grab every publisher, then loop through them using `get_organization` to check for missing metadata. If a fetch fails, the built-in exponential backoff handles the retry automatically. You can chain these operations to build a nightly sync job. The agent calls `newest_datasets` to find recent uploads, processes the metadata, and pushes the updates to your internal database. Conditional branching ensures that malformed dataset entries get flagged for human review instead of breaking the pipeline.

Build Resilient Data Pipelines

The `preview_data` tool pulls raw rows from specific resources. Your agent can ingest these records and run validation checks against expected formats. You pair this with `show_columns` to verify that the schema hasn't changed since the last run. If the schema differs, Mastra's workflow engine can route the execution to a different branch. It might alert a Slack channel or attempt to map the new columns dynamically before saving the data.

Execute Complex SPARQL Chains

The `query_sparql` tool queries semantic endpoints like Aragopedia or ELI. Your agent constructs the query based on upstream workflow data and fires it off. Semantic queries often fail due to syntax errors. Because this MCP server handles the connection directly, you can design a Mastra step that catches `query_sparql` errors, feeds the failure reason back into the agent, and attempts a corrected query up to three times before escalating to a developer.

Setup guide

Set up Aragón Open Data MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Aragón Open Data tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "aragon-open-data-mcp-client",
  servers: {
    "aragon-open-data-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Aragón Open Data Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Aragón Open Data tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Aragón Open Data transactions"
);
console.log(result.text);

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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 Mastra AI

Install `@mastra/mcp@latest`. Instantiate the client with `new MCPClient()` and pass your Vinkius endpoint URL. Spread the tools from `listTools()` into your agent configuration.

The framework handles HTTP 429 errors natively. If you hit a limit while looping through `list_datasets`, the workflow pauses and retries with exponential backoff.

Pass the `query_sparql` tool to an agent step. The agent will formulate the query, execute it, and pass the resulting JSON array to the next step in your workflow.

You can enable `requireToolApproval` for any tool. This is useful if you want a human to review the SPARQL query generated by the agent before it executes.

The MCP Server only accesses public CKAN metadata, tags, and demographic or geographic datasets. The Vinkius infrastructure routes your requests through ephemeral, zero-trust sandboxes that drop all state the moment your workflow completes.

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