Compensation Professional — compensation library, answers, and wage-compliance data

Check the binding minimum wage for multiple locations

check_wage_locations

Read-only

Checks the binding minimum-wage rate for up to 50 US locations in one call (city/county rate when it applies, else state, else federal). Each location may be a place name ("Spokane, WA", "Texas", "TX") or a jurisdiction slug. A city with no rule of its own on file returns its state's rate with noLocalRule:true. An unmatched location returns found:false with a reason instead of being omitted. 113 known jurisdictions, e.g.: federal, alaska, alabama, arkansas.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`locations`	Yes	Up to 50 places, one per item: "City, ST", a state name or code, or a slug (e.g. Spokane, WA; texas; seattle-wa).
`rangeMinimum`	No	Optional proposed hourly range minimum; each found row returns belowRangeMinimum (CP-129/130).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`found`	Yes
`source`	Yes
`results`	Yes
`contract`	Yes
`requested`	Yes
`retrievedAt`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed7 schema fields changedSep 28, 2026

changedInput schema / properties / locations / description

Previous value: -"Up to 50 jurisdiction slugs (e.g. seattle-wa, denver-co, texas)."New value: +"Up to 50 places, one per item: \"City, ST\", a state name or code, or a slug (e.g. Spokane, WA; texas; seattle-wa)."

addedInput schema / properties / rangeMinimum

Added value: +{
+  "description": "Optional proposed hourly range minimum; each found row returns belowRangeMinimum (CP-129/130).",
+  "exclusiveMinimum": 0,
+  "type": "number"
+}

addedOutput schema / properties / results / items / properties / belowRangeMinimum

Added value: +{
+  "additionalProperties": false,
+  "properties": {
+    "next": {
+      "type": "boolean"
+    },
+    "nextEffective": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "now": {
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "now",
+    "next",
+    "nextEffective"
+  ],
+  "type": "object"
+}

addedOutput schema / properties / results / items / properties / candidates

Added value: +{
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

addedOutput schema / properties / results / items / properties / didYouMean
```
Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}
```
addedOutput schema / properties / results / items / properties / noLocalRule
```
Added value: +{
+  "type": "boolean"
+}
```

addedOutput schema / properties / results / items / properties / reason

Added value: +{
+  "enum": [
+    "not_recognised",
+    "ambiguous"
+  ],
+  "type": "string"
+}

AddedSep 25, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already establish the read-only, non-destructive, closed-world profile, so the bar is lower; the description still adds substantial behavioral detail beyond them: fallback precedence, the noLocalRule:true signal for cities without their own rule, and the fact that unmatched locations return found:false with a reason rather than being silently dropped. That is exactly the edge-case disclosure an agent needs to interpret partial results.

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 scope and precedence constraint are front-loaded in the first sentence, with the format guidance, edge-case behavior, and jurisdiction examples following in a tight sequence. No sentence is filler; each adds a distinct fact an agent would otherwise have to guess.

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?

An output schema exists, so return shapes need not be spelled out, yet the description still explains the two outcome flags that determine how an agent reads the response. Combined with accepted input formats and the batch limit, nothing needed to invoke this tool correctly is missing.

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%, so the baseline would be 3, but the description adds real meaning by enumerating the accepted location forms ("Spokane, WA", "Texas", "TX") and jurisdiction slugs, which the schema only partially conveys. The second parameter, rangeMinimum, is left entirely to the schema, keeping this from a 5.

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 specific verb and resource (checks the binding minimum-wage rate) and a precise scope (up to 50 US locations in one call), plus the resolution precedence it applies. This is clearly distinguishable from siblings like get_wage_jurisdiction, which presumably handles a single jurisdiction.

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?

It makes the batch use case explicit ('up to 50 US locations in one call') and explains the city/county → state → federal resolution order, which tells an agent when a location will resolve to a broader rate. It does not explicitly name or exclude alternatives such as get_wage_jurisdiction, so the routing guidance stops just short of full when/when-not framing.

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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