Skip to main content
Glama

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'.

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds the weekly cadence and filtering capabilities, which is useful context but does not reveal additional behaviors such as response format, pagination, or rate limits. With the annotation coverage, 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 with no wasted words: the first states the data source and cadence, the second lists filters and use cases. Information is front-loaded and every sentence 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 simple read-only data retrieval tool with five self-documenting parameters and no required fields, the description plus schema covers what an agent needs. The description adds use cases and filter semantics. It does not mention output format, but with no output schema and a straightforward dataset this is not a significant gap.

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. The description reinforces the region values (PADD1-PADD5 or national) and grade list, but it adds little beyond what the schema provides. This matches the baseline for full schema coverage.

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 identifies the resource as weekly US retail gasoline prices from EIA and mentions the key filters (region, grade), which distinguishes it from sibling EIA tools covering electricity, natural gas, and oil supply. It lacks an explicit action verb like 'retrieve' or 'get', relying instead on the noun phrase 'Weekly US retail gasoline prices', so it is clear but not maximally specific.

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 description gives concrete use cases — fuel-cost analysis, transportation logistics, and consumer price tracking — which imply when this tool is appropriate. However, it does not explicitly contrast with alternatives like eia_series_lookup, eia_oil_supply, or other EIA tools, nor does it state when not to use it. Usage context is present but not exclusionary.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources