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eia_gasoline_prices

Read-onlyIdempotent

Weekly US retail gasoline prices from EIA. Filter by region (PADD1-PADD5 or national) and grade (regular, midgrade, premium, diesel, all). Useful for fuel-cost analysis, transportation logistics, and consumer price tracking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoInclusive upper-bound period (ISO date or YYYY-MM).
gradeNoFuel grade: 'all', 'regular', 'midgrade', 'premium', 'diesel'. Default 'all'.
limitNoMaximum rows to return (default 50, max 5000).
startNoInclusive lower-bound period (ISO date or YYYY-MM depending on series cadence).
regionNoPADD region code or 'national'. Examples: 'national', 'PADD1', 'PADD3'. Default 'national'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / limit / maximum
      Added value: +5000
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • changedInput schema / properties / limit / type
      Previous value: -"number"New value: +"integer"
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=true, so the safety profile is fully covered. The description adds the weekly cadence and the available filter dimensions, but says nothing about rate limits, pagination behavior, or result shape. With annotations doing the heavy lifting, this is adequate but not rich.

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, zero waste, with the resource and cadence front-loaded and the filter dimensions immediately after. Every clause earns its place.

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?

For a read-only, all-optional-parameter data retrieval tool with no output schema, the description covers the resource, cadence, and both filter axes. Only the return structure/pagination story is unaddressed, which is a minor gap given the annotations.

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 description coverage is 100%, so the schema already documents all five parameters including defaults and examples. The description's mention of PADD1-PADD5/national and the grade values largely restates what the schema descriptions already provide, so baseline 3 applies.

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 states a specific verb+resource ('Weekly US retail gasoline prices from EIA') and names the two filterable dimensions, so the agent knows exactly what data it returns. It does not, however, differentiate itself from the many sibling EIA tools (eia_oil_supply, eia_natural_gas, eia_series_lookup), so routing relies on the resource noun alone.

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?

'Useful for fuel-cost analysis, transportation logistics, and consumer price tracking' gives domain context but no when-to-use/when-not or alternative selection guidance relative to sibling EIA endpoints. Usage is implied rather than stated.

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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