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ONS + Nomis MCP server

ONS + Nomis MCP server

by brynmrgn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Remote

get_dimensions

Retrieve the dimensions (variables) of a specific dataset version, including time, geography, and topic dimensions, with the number of available options per dimension.

Instructions

List the dimensions (variables) of a dataset version.

Every dataset has time and geography; the rest are topic dimensions. number_of_options tells you how many codes each holds. Feed a dimension name into get_options to see valid codes.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>",
      "edition": "<edition>",
      "version": "<version>"
    },
    "name": "get_dimensions"
  }
}

Input Schema

NameRequiredDescriptionDefault
editionYes
versionYes
dataset_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It explains that every dataset has 'time' and 'geography' dimensions, and that 'number_of_options' indicates code counts. This gives insight into the return structure without needing to guess, though it doesn't address limitations or side effects (which are likely absent).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only three sentences, each with a clear purpose: state the main function, provide additional context about dimensions, and guide to a sibling tool. No unnecessary words or redundancy, making it succinct and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the essential purpose and key outputs (mentioning time, geography, and number_of_options). An output schema exists but is not shown; the description complements it well. It could be slightly richer (e.g., noting that it returns a list), but overall it is sufficient for a list operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has three required parameters with zero description coverage. The tool description does not explain these parameters (e.g., what dataset_id, edition, version represent or their expected formats). Given the low schema coverage, the description fails to compensate, providing no semantic help for parameter usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'List the dimensions (variables) of a dataset version', which clearly defines the action and resource. It distinguishes from siblings by mentioning get_options as a follow-up, making the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides good context on when to use the tool (to explore dataset dimensions) and what to do next (use get_options). However, it lacks explicit guidance on when not to use it or alternatives among the many sibling tools, so it doesn't fully satisfy this dimension.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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