How to Use the Aragón Open Data MCP in CrewAI

Q: How do I add Aragón Open Data to my CrewAI setup?

Use MCPServerHTTP from crewai.mcp. Pass your Vinkius URL to connect the MCP client, then assign the resulting tools to specific agents in your crew.

Q: Can CrewAI agents find the most popular Aragón Open Data sets?

Yes. Assign the mostdownloadeddatasets tool to a research agent. The agent can pull the top lists and use that data to guide its subsequent queries.

Q: How do multiple CrewAI agents share Aragón Open Data results?

When one agent runs getdataset or querysparql, the JSON output enters the crew's shared memory. Subsequent agents read that memory to perform their specific tasks.

Q: Can I restrict which tools this MCP Server exposes to an agent?

Absolutely. Use the toolfilter parameter when setting up the client to give your Researcher agent access to searchdatasets while keeping querysparql restricted to your Semantic agent.

Deploy specialized autonomous agents to research, analyze, and monitor civic datasets from Aragón Open Data using CrewAI.

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Connect Aragón Open Data MCP to CrewAI

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

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Deploy Aragón Open Data Teams

The `search_datasets` tool exposes the entire CKAN catalog to your multi-agent setups. You can assign a Researcher agent to use this tool alongside `list_tags` to find relevant civic information. The Researcher gathers the raw metadata and passes it into the shared memory pool. An Analyst agent picks up that metadata and uses `get_dataset` to dig into the specifics. Because CrewAI handles the handoff, you build autonomous teams that hunt down specific government records without you lifting a finger.

Extract and Preview Records

The `preview_data` tool returns up to 1000 records from a given resource. A Data Extraction agent can call this tool to pull raw rows, while concurrently using `show_columns` to understand the data structure. You can configure a Moderator agent to watch this extraction process. If the extracted data contains unexpected null values, the Moderator can instruct the Extraction agent to try a different view using `list_views` or flag the dataset for human review.

Run Queries via MCP Server

The `query_sparql` tool connects your agents to ontologies like EI2A and Aragopedia. A specialized Semantic agent can translate natural language questions into valid SPARQL syntax and execute the query directly. A Reporting agent takes the JSON response from that query and drafts a plain-English summary. This MCP setup turns complex semantic web endpoints into simple conversational answers for your end users.

Setup guide

Set up Aragón Open Data MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Aragón Open Data tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Aragón Open Data Analyst",
    goal="Access and analyze Aragón Open Data data via MCP.",
    backstory="Expert analyst with direct Aragón Open Data access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Aragón Open Data transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Aragón Open Data Analyst",
        goal="Access and analyze Aragón Open Data data via MCP.",
        backstory="Expert analyst with direct Aragón Open Data access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Aragón Open Data transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

Use `MCPServerHTTP` from `crewai.mcp`. Pass your Vinkius URL to connect the MCP client, then assign the resulting tools to specific agents in your crew.

Yes. Assign the `most_downloaded_datasets` tool to a research agent. The agent can pull the top lists and use that data to guide its subsequent queries.

When one agent runs `get_dataset` or `query_sparql`, the JSON output enters the crew's shared memory. Subsequent agents read that memory to perform their specific tasks.

Absolutely. Use the `tool_filter` parameter when setting up the client to give your Researcher agent access to `search_datasets` while keeping `query_sparql` restricted to your Semantic agent.

The tools strictly interact with public government records, ontologies, and dataset schemas. Vinkius processes all CrewAI requests through isolated, ephemeral environments that require only a single endpoint token, ensuring no persistent access remains after the task finishes.

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