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List upcoming minimum wage increases

list_upcoming_increases
Read-onlyIdempotent

Retrieve scheduled US state minimum wage increases from a chosen date through 2027, including new, previous, and tipped rates, with optional state filtering.

Instructions

Every scheduled state minimum wage change from a date (default today) through 2027-12-31, sorted by date, with the new rate, the previous rate, the change and the tipped cash wage. Includes 2027-01-01 increases already enacted in law or announced under inflation indexing, and lists indexed changes whose number is not published yet as announced:false. Optional filter by states.

Input Schema

NameRequiredDescriptionDefault
fromNoStart date as YYYY-MM-DD (exclusive). Defaults to today.
statesNoOptional list of states to include.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's value is in the extra behavior it discloses: results are date-sorted, include enacted and inflation-indexed 2027-01-01 changes, and mark unpublished indexed changes as announced:false. That announced:false nuance is genuinely useful for an agent reasoning about missing numbers; only pagination/volume behavior is unaddressed.

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?

Two dense sentences with the scope and window front-loaded and no filler. The second sentence is clause-heavy ('already enacted in law or announced under inflation indexing') but each clause carries real behavioral meaning, so nothing is wasted.

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?

With no output schema, the description carries the return-value burden and does so fully: new rate, previous rate, change, tipped cash wage, sort order, and the announced:false edge case. Combined with complete parameter coverage, an agent has everything needed to call and interpret it.

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?

Schema coverage is 100%, so the baseline is 3. The description restates the 'from' default and the optional state filter but adds nothing about the schema's exclusive boundary on 'from' or the accepted state name/code formats, which remain schema-only details.

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?

States a specific verb and resource ('Every scheduled state minimum wage change'), plus the exact temporal window (from a date through 2027-12-31), the sort order, and the returned fields. An agent can distinguish this forward-looking schedule tool from get_minimum_wage or compare_states without opening any schema.

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

Usage Guidelines3/5

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

The temporal scope and the 'default today' behavior imply when the tool is appropriate, and the optional states filter is noted. However, it never names an alternative or states an exclusion, so the boundary with get_minimum_wage (presumably current rates) is left to inference.

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