Uk Ons

Get Observations

get_observations

Read-onlyIdempotent

Fetch observations from a dataset/edition/version. Pass dimension filters as dimensions map ({"geography": "K02000001", "time": "2023"}). Use * for "all values" within a dimension.

Input Schema

TableJSON Schema

Name	Required	Description
`edition`	Yes	Edition label
`version`	Yes	Version
`dataset_id`	Yes	ONS dataset ID
`dimensions`	Yes	Dimension code → value (or "*" for all). Example: {"geography":"K02000001","time":"2023"}.

Output Schema

TableJSON Schema

Name	Required	Description
`notes`	Yes	Usage notes
`edition`	Yes	Edition label
`version`	Yes	Version
`dataset_id`	Yes	Dataset ID
`observations`	Yes	Observation rows
`unit_of_measure`	Yes	Unit of measurement
`total_observations`	Yes	Total number of observations

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedMay 22, 2026

addedInput schema / examples

Added value: +[
+  {
+    "dataset_id": "cpih01",
+    "dimensions": {
+      "geography": "K02000001",
+      "time": "2023"
+    },
+    "edition": "time-series",
+    "version": "1"
+  },
+  {
+    "dataset_id": "ashe-table-1",
+    "dimensions": {
+      "geography": "*",
+      "time": "2023"
+    },
+    "edition": "annual",
+    "version": "2"
+  }
+]

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "dataset_id": {
+      "description": "Dataset ID",
+      "type": "string"
+    },
+    "edition": {
+      "description": "Edition label",
+      "type": "string"
+    },
+    "notes": {
+      "description": "Usage notes",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "observations": {
+      "description": "Observation rows",
+      "items": {
+        "properties": {
+          "dimensions": {
+            "additionalProperties": {
+              "properties": {
+                "id": {
+                  "description": "Dimension value ID",
+                  "type": [
+                    "string",
+                    "null"
+                  ]
+                },
+                "label": {
+                  "description": "Dimension value label",
+                  "type": [
+                    "string",
+                    "null"
+                  ]
+                }
+              },
+              "type": "object"
+            },
+            "description": "Dimension codes and labels for this observation",
+            "type": "object"
+          },
+          "value": {
+            "description": "Observation value",
+            "type": [
+              "string",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_observations": {
+      "description": "Total number of observations",
+      "type": [
+        "integer",
+        "null"
+      ]
+    },
+    "unit_of_measure": {
+      "description": "Unit of measurement",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "version": {
+      "description": "Version",
+      "type": "string"
+    }
+  },
+  "required": [
+    "dataset_id",
+    "edition",
+    "version",
+    "total_observations",
+    "unit_of_measure",
+    "notes",
+    "observations"
+  ],
+  "type": "object"
+}

First observedMay 13, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so description adds no new behavioral traits. The description is consistent but does not disclose potential limits or auth requirements.

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?

Two sentences, front-loaded with purpose, no unnecessary words.

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?

Covers essential usage for a data-fetching tool. Does not explain error handling or output format (output schema exists), but is adequate for the complexity.

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

Parameters4/5

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

Schema coverage is 100%, but description adds meaning by explaining the map format for 'dimensions' and the wildcard usage, complementing the schema.

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 uses specific verb+resource ('Fetch observations from a dataset/edition/version') and distinguishes from sibling tools like 'get_dataset' and 'get_version' by focusing on data retrieval with dimension filters.

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?

Provides clear instructions for passing dimension filters and using '*' for all values. Does not explicitly state when to use vs. alternatives, but context is sufficient.

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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