econdata

Get Unemployment

get_unemployment

Read-onlyIdempotent

Get the US civilian unemployment rate over time (Bureau of Labor Statistics) — the percentage of the labor force currently unemployed. Use this for "unemployment rate" / "jobless rate" queries. Returns monthly values by year and month.

Input Schema

TableJSON Schema

Name	Required	Description
`_apiKey`	No	Optional BLS registration key (free, raises the daily cap from ~25 to 500). The gateway supplies a platform key; pass your own only to override.
`end_year`	No	End year as 4-digit string (e.g. "2024"). Optional.
`start_year`	No	Start year as 4-digit string (e.g. "2020"). Optional.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Monthly unemployment rate data
`unit`	Yes	Unit of measurement (percent)
`total`	Yes	Number of data points returned. Equal to `returned` — BLS returns every point in the requested year range.
`end_year`	Yes	End year filter if provided, null otherwise
`returned`	No	How many data points are in `data`. Always equal to `total` here; stated so a caller need not assume it.
`series_id`	Yes	BLS series ID (LNS14000000)
`start_year`	Yes	Start year filter if provided, null otherwise
`description`	Yes	Series description
`observation_order`	No	Order of the `data` array. BLS returns each series newest-first.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed3 schema fields changedAug 31, 2026

addedOutput schema / properties / observation_order

Added value: +{
+  "description": "Order of the `data` array. BLS returns each series newest-first.",
+  "enum": [
+    "newest_first"
+  ],
+  "type": "string"
+}

addedOutput schema / properties / returned

Added value: +{
+  "description": "How many data points are in `data`. Always equal to `total` here; stated so a caller need not assume it.",
+  "type": "integer"
+}

changedOutput schema / properties / total / description

Previous value: -"Total number of data points returned"New value: +"Number of data points returned. Equal to `returned` — BLS returns every point in the requested year range."

Changed1 schema field changedJun 24, 2026

addedInput schema / properties / _apiKey

Added value: +{
+  "description": "Optional BLS registration key (free, raises the daily cap from ~25 to 500). The gateway supplies a platform key; pass your own only to override.",
+  "type": "string"
+}

Changed1 schema field changedMay 8, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "Monthly unemployment rate data",
+      "items": {
+        "properties": {
+          "month": {
+            "description": "Month name (e.g., January)",
+            "type": "string"
+          },
+          "period": {
+            "description": "Period code (e.g., M01)",
+            "type": "string"
+          },
+          "rate": {
+            "description": "Unemployment rate as percentage",
+            "type": "number"
+          },
+          "year": {
+            "description": "Year of the observation",
+            "type": "string"
+          }
+        },
+        "required": [
+          "year",
+          "month",
+          "period",
+          "rate"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "description": {
+      "description": "Series description",
+      "type": "string"
+    },
+    "end_year": {
+      "description": "End year filter if provided, null otherwise",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "series_id": {
+      "description": "BLS series ID (LNS14000000)",
+      "type": "string"
+    },
+    "start_year": {
+      "description": "Start year filter if provided, null otherwise",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "total": {
+      "description": "Total number of data points returned",
+      "type": "integer"
+    },
+    "unit": {
+      "description": "Unit of measurement (percent)",
+      "type": "string"
+    }
+  },
+  "required": [
+    "series_id",
+    "description",
+    "unit",
+    "start_year",
+    "end_year",
+    "total",
+    "data"
+  ],
+  "type": "object"
+}

Changed1 schema field changedMay 6, 2026

addedInput schema / examples

Added value: +[
+  {
+    "end_year": "2024",
+    "start_year": "2019"
+  }
+]

First observedApr 14, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and a non-destructive nature. The description adds useful behavioral context beyond that: it describes BLS as the data source, the civilian unemployment rate as the metric, and monthly granularity over time. No contradictions 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.

Conciseness5/5

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

Two tight sentences: the first states the core purpose and metric, and the second gives query-language guidance plus return frequency. No filler or redundancy.

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

Completeness5/5

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

For a simple, read-only tool with all parameters optional and fully described, an output schema present, and annotations covering safety behavior, the description is complete. It gives the data source, the exact metric, queried phrasing, and the output granularity.

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

Parameters3/5

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

The input schema has 100% description coverage: _apiKey, start_year, and end_year each have meaningful descriptions. The tool description adds little about parameters, so the baseline score of 3 is appropriate; it does not need to repeat schema content.

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 names a precise verb ('Get'), a specific resource ('US civilian unemployment rate'), a data source ('Bureau of Labor Statistics'), and the exact metric ('percentage of the labor force currently unemployed'). This clearly distinguishes it from related tools like get_cpi or get_employment_by_industry.

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 explicitly says to use it for 'unemployment rate' / 'jobless rate' queries, which gives clear context for when this tool is appropriate. It does not name sibling alternatives or state exclusions, so it stops short of the strongest guidance.

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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