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

Q: How do I structure a multi-step query using UK ONS Discovery — Search 337+ Statistical Datasets and LangChain?

You chain the calls together. First, let your agent call searchdatasets to get the ID. Then, it uses getdimensions with that ID to gather filter options. Finally, it runs getobservations, passing all gathered parameters for a complete query.

Q: Can LangChain use UK ONS Discovery — Search 337+ Statistical Datasets to find metadata?

Absolutely. You can call listdatasets or searchdatasets to browse the catalog. If you need specific technical details, getdatasetinfo provides dimensions, editions, and versions for any given dataset ID.

Q: How does LangChain handle filtering with UK ONS Discovery — Search 337+ Statistical Datasets?

The agent uses a two-step filter process. It first calls getdimensionoptions to list all valid filters for a specific dimension. Then, it passes those validated options into the final getobservations tool call.

Q: Does UK ONS Discovery — Search 337+ Statistical Datasets support full time series retrieval with LangChain?

Yes. When calling getobservations, you simply set the dimension filter for time to . This pulls the entire available time series for that dataset and those dimensions.

Build Complex Data Pipelines for LangChain using UK ONS Discovery — Search 337+ Statistical Datasets

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

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Perplexity

Multi-Step Dataset Retrieval

You can build full reasoning pipelines. Start by calling `search_datasets` to find the exact dataset ID you need, even if you only know keywords. After finding the ID, use `get_dimensions` and `get_dimension_options` to map out all available filters—like geography or time periods—before finally executing a query with `get_observations`.

Data Catalog Discovery

Need to know what data exists? Run `list_datasets` to browse the full catalog of 337+ ONS datasets. This gives your agent the scope it needs before trying to query anything specific. If you need a deep dive on one dataset, call `get_dataset_info`. It reveals metadata like dimensions and versions so your chain doesn't guess which parameters to use.

Targeted Observation Querying

This setup lets your agent query specific stats with precision. After getting the dataset ID, you can narrow down filters using `get_dimensions` to see exactly what options are available. The final step is running `get_observations`, passing both the required ID and those precise dimension filters. It gives clean results for any ONS statistical data.

Setup guide

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

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes UK ONS Discovery — Search 337+ Statistical Datasets tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "uk-ons-discovery-search-337-statistical-datasets-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent UK ONS Discovery — Search 337+ Statistical Datasets transactions"
    })
    print(result["messages"][-1].content)

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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 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 LangChain

You chain the calls together. First, let your agent call `search_datasets` to get the ID. Then, it uses `get_dimensions` with that ID to gather filter options. Finally, it runs `get_observations`, passing all gathered parameters for a complete query.

Absolutely. You can call `list_datasets` or `search_datasets` to browse the catalog. If you need specific technical details, `get_dataset_info` provides dimensions, editions, and versions for any given dataset ID.

The agent uses a two-step filter process. It first calls `get_dimension_options` to list all valid filters for a specific dimension. Then, it passes those validated options into the final `get_observations` tool call.

Yes. When calling `get_observations`, you simply set the dimension filter for time to `*`. This pulls the entire available time series for that dataset and those dimensions.

This server touches statistical datasets containing demographic, economic, health, and trade information. It is structured to allow querying raw ONS observations using specific dimension filters.

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