How to Use the UK ONS Discovery — Search 337+ Statistical Datasets MCP in CrewAI

Q: What is the best way to query a dataset using CrewAI?

Start by letting one agent run searchdatasets to confirm the ID. Then, let another agent use getdimensions and finally, have your action agent call getobservations.

Build autonomous research crews for UK ONS Discovery — Search 337+ Statistical Datasets using CrewAI.

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Connect UK ONS Discovery — Search 337+ Statistical Datasets MCP to CrewAI

Create your Vinkius account to connect UK ONS Discovery — Search 337+ Statistical Datasets 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 UK ONS Discovery — Search 337+ Statistical Datasets with CrewAI

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Perplexity

Autonomous Data Research Teams

Don't run a single agent; deploy a team. One specialized Agent researches the catalog using `search_datasets`, while another uses `get_dimensions` to validate filter options. A third 'Action' agent then takes those validated parameters and calls `get_observations` to retrieve the final statistical dataset.

Structured Metadata Analysis

Need to know what data is available? Assign a role to discover metadata. An Agent can call `get_dataset_info` on an ONS ID, gathering details about dimensions and editions. This lets your crew build a knowledge base of the 337+ statistical datasets without needing constant human oversight.

Targeted Dataset Discovery

The `get_dimension_options` tool helps agents narrow down possibilities. Instead of brute-forcing filters, an agent can ask for all available options (like time periods or geographies) for a specific dimension. This makes your crew's data gathering much more efficient and targeted.

Setup guide

Set up UK ONS Discovery — Search 337+ Statistical Datasets 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 UK ONS Discovery — Search 337+ Statistical Datasets tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="UK ONS Discovery — Search 337+ Statistical Datasets Analyst",
    goal="Access and analyze UK ONS Discovery — Search 337+ Statistical Datasets data via MCP.",
    backstory="Expert analyst with direct UK ONS Discovery — Search 337+ Statistical Datasets access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent UK ONS Discovery — Search 337+ Statistical Datasets 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="UK ONS Discovery — Search 337+ Statistical Datasets Analyst",
        goal="Access and analyze UK ONS Discovery — Search 337+ Statistical Datasets data via MCP.",
        backstory="Expert analyst with direct UK ONS Discovery — Search 337+ Statistical Datasets access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent UK ONS Discovery — Search 337+ Statistical Datasets 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 UK ONS. 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 UK ONS Discovery — Search 337+ Statistical Datasets MCP in CrewAI

You assign roles: one agent searches the catalog, a second validates dimensions, and a third executes the final query. The crew shares memory, passing the validated IDs and filters sequentially.

Absolutely. Since the MCP Server covers economy, population, health, and trade, your crew can be specialized to pull structured statistical datasets from any domain in the catalog.

Start by letting one agent run `search_datasets` to confirm the ID. Then, let another agent use `get_dimensions` and finally, have your action agent call `get_observations`.

The catalog contains 337+ datasets ready for discovery. Your crew just needs to know which tools to call to find the right one.

This MCP Server touches statistical datasets covering UK economic metrics, population figures, health statistics, and trade data.

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