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

Q: How do I index a complex data retrieval sequence with UK ONS Discovery — Search 337+ Statistical Datasets and LlamaIndex?

You run the query using getobservations, and then pass the structured results into your vector store. The knowledge base learns not just the answer, but the specific filters (ID + dimensions) that led to it.

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

Yes. By indexing the results of getdimensions and getdimensionoptions, you create a living record of available filters. Your agent can query this index to confirm if a requested filter value is valid.

Index UK ONS Discovery — Search 337+ Statistical Datasets Results into LlamaIndex Knowledge Bases

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

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

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Indexing Dataset Metadata

You can index the structure of the data itself. Calling `get_dataset_info` captures metadata—like dimensions and versions—which are then added to your searchable knowledge base. This means you'll get answers grounded in the schema, not just the raw numbers, making your RAG app much more reliable.

Building Queryable Catalog Indices

Use `search_datasets` to find matching ONS datasets by keyword. The output (ID, title, description) becomes a searchable document in your index. When you need to guide the user, indexing results from `list_datasets` ensures that even browsing metadata is available for semantic retrieval.

Creating Filter-Grounded Knowledge

The most powerful feature is making filters searchable. You use `get_dimension_options` to pull all valid filter values (e.g., geography codes). This list gets indexed. The next time you query, your agent can reference this knowledge base to know exactly which dimensions and options are possible for an ONS dataset.

Setup guide

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

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all UK ONS Discovery — Search 337+ Statistical Datasets MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to UK ONS Discovery — Search 337+ Statistical Datasets tools.",
)
response = await agent.run("List recent UK ONS Discovery — Search 337+ Statistical Datasets data")

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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 LlamaIndex

You run the query using `get_observations`, and then pass the structured results into your vector store. The knowledge base learns not just the answer, but the specific filters (ID + dimensions) that led to it.

Yes. By indexing the results of `get_dimensions` and `get_dimension_options`, you create a living record of available filters. Your agent can query this index to confirm if a requested filter value is valid.

The API results are converted into embeddings and stored in the vector database. This means you can query past sessions or configurations, getting answers grounded in actual ONS dataset IDs and titles.

Yes. When you query for observations by setting the dimension filter to `*`, those resulting time series are indexed. You can then ask semantic questions like, 'What were population trends in region X over the last decade?'

This server touches statistical datasets covering demographic and economic figures from ONS. The structured API output allows you to index metadata about these specific dataset IDs and their dimensions.

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