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

Q: How do I make AutoGen debate data retrieval methods with UK ONS Discovery — Search 337+ Statistical Datasets?

You assign roles: one agent searches using searchdatasets, another checks the full catalog via listdatasets. They debate which dataset ID is most relevant, leading to a consensus on the right data source.

Q: Can AutoGen use UK ONS Discovery — Search 337+ Statistical Datasets for schema validation?

Yes. Agents can compare results from getdimensions against getdimensionoptions. If the options don't match what was expected, they flag an error and request clarification before proceeding with a query.

Facilitate Consensus Decisions with AutoGen using UK ONS Discovery — Search 337+ Statistical Datasets

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

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Setup UK ONS Discovery — Search 337+ Statistical Datasets with AutoGen

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Perplexity

Debating Dataset Selection

You build a system where multiple agents debate the best approach. One agent might run `search_datasets` to find potential candidates, while another challenges that result by using `list_datasets` for a complete catalog review. The final decision (the dataset ID) is reached through consensus after reviewing both sets of findings.

Negotiating Query Parameters

When querying data, agents can debate the necessary filters. One might use `get_dimensions` to identify all possible dimensions. A second agent then uses `get_dimension_options` to validate a specific set of filter values for `get_observations`. The system converges on the most precise combination before executing the query.

Validating Data Structure and Scope

Agents can cross-check data integrity. One agent calls `get_dataset_info` to check dimensions, while a second queries `get_dimension_options` for those same dimensions. This process validates the schema against multiple sources. The final output is only accepted when all agents agree on the necessary parameters.

Setup guide

Set up UK ONS Discovery — Search 337+ Statistical Datasets MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes UK ONS Discovery — Search 337+ Statistical Datasets tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="UK ONS Discovery — Search 337+ Statistical Datasets_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent UK ONS Discovery — Search 337+ Statistical Datasets data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="UK ONS Discovery — Search 337+ Statistical Datasets_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent UK ONS Discovery — Search 337+ Statistical Datasets data")
    print(result.messages[-1].content)

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 AutoGen

You assign roles: one agent searches using `search_datasets`, another checks the full catalog via `list_datasets`. They debate which dataset ID is most relevant, leading to a consensus on the right data source.

Yes. Agents can compare results from `get_dimensions` against `get_dimension_options`. If the options don't match what was expected, they flag an error and request clarification before proceeding with a query.

Agents can collaboratively build this. One agent determines the dataset ID, another sets the dimension filter to `*`, and a third executes `get_observations` to retrieve the complete time series.

The multi-agent system forces deliberation. The conflicting conclusions are passed back into the loop until a specific, validated set of dimension filters is agreed upon and used in the final `get_observations` call.

The server handles statistical datasets containing economic, population, and health statistics from ONS. The tools allow agents to query specific dimensions and observation records based on IDs found via `search_datasets`.

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