Adzuna

History

history

Read-onlyIdempotent

"Job market trends" / "hiring trends in [category]" / "salary trends for [role]" / "is [field] hiring more / less than last year" — historical monthly time series for job volume and mean salary in a country, optionally filtered by location and category. Use for labor-market analysis, recession indicators, hiring-cycle research.

Input Schema

Name	Required	Description
`months`	No	1-100 (default 12)
`country`	Yes
`category`	No	Adzuna category tag (from `categories` tool)
`location`	No

Output Schema

Name	Required	Description	Default
`month`	No	Average salary keyed by month, e.g. {"2026-07": 42537.34}. An OBJECT map, not an array — verified against a live response.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedAug 13, 2026

addedOutput schema / properties / month / additionalProperties
```
Added value: +{
+  "type": "number"
+}
```

changedOutput schema / properties / month / description

Previous value: -"Monthly time series data"New value: +"Average salary keyed by month, e.g. {\"2026-07\": 42537.34}. An OBJECT map, not an array — verified against a live response."

removedOutput schema / properties / month / items

Removed value: -{
-  "properties": {
-    "avg_salary": {
-      "description": "Average salary for month",
-      "type": "number"
-    },
-    "date": {
-      "description": "Month in YYYY-MM format",
-      "type": "string"
-    },
-    "posting_count": {
-      "description": "Total jobs posted in month",
-      "type": "number"
-    }
-  },
-  "type": "object"
-}

changedOutput schema / properties / month / type

Previous value: -"array"New value: +"object"

Changed2 schema fields changedMay 24, 2026

addedInput schema / examples

Added value: +[
+  {
+    "country": "gb",
+    "months": 24
+  },
+  {
+    "category": "IT Jobs",
+    "country": "us",
+    "location": "New York",
+    "months": 12
+  }
+]

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "month": {
+      "description": "Monthly time series data",
+      "items": {
+        "properties": {
+          "avg_salary": {
+            "description": "Average salary for month",
+            "type": "number"
+          },
+          "date": {
+            "description": "Month in YYYY-MM format",
+            "type": "string"
+          },
+          "posting_count": {
+            "description": "Total jobs posted in month",
+            "type": "number"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}

First observedMay 13, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already cover safety (readOnly, openWorld, idempotent, non-destructive). The description adds useful context about the output being a monthly time series of job volume and mean salary, but does not describe return format or limitations. No contradiction with annotations.

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

Conciseness4/5

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

The description is moderately sized and well-structured: example queries, then the core explanation, then use cases. It is slightly dense but each component adds value without redundancy.

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?

With an output schema and safety annotations present, the description sufficiently covers purpose, parameter examples, and intended use cases. It could be improved by explicitly contrasting with sibling tools like salary_histogram, but it is adequate for correct selection and invocation.

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 description coverage is 50% (months and category). The description adds semantic value by clarifying 'country' as the geographic scope and providing concrete example values ('gb', 'us', 'New York') that help with parameter format. The 'months' parameter is left to the schema, which already documents it.

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

Purpose4/5

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

The description clearly states the tool returns 'historical monthly time series for job volume and mean salary in a country, optionally filtered by location and category,' identifying the resource and scope. It provides illustrative query examples but does not explicitly differentiate from sibling tools like salary_histogram.

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 gives explicit use cases: 'Use for labor-market analysis, recession indicators, hiring-cycle research.' It does not mention when not to use the tool or name alternative tools, so it misses the highest bar.

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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