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cyntrica

Gov Data MCP

by cyntrica

eia_petroleum

Read-only

Retrieve petroleum prices from EIA: crude oil (WTI/Brent), gasoline, diesel, heating oil. Specify product, frequency, and date range to access historical or current data.

Instructions

Get petroleum/oil prices — crude oil spot prices (WTI, Brent), retail gasoline prices, diesel, heating oil.

Product codes:

  • EPCBRENT: Brent crude oil spot price

  • EPCWTI: WTI crude oil spot price

  • EMM_EPMRU_PTE_NUS_DPG: US regular gasoline retail

  • EMM_EPMPU_PTE_NUS_DPG: US premium gasoline retail

  • EMD_EPD2D_PTE_NUS_DPG: US diesel retail

  • EER_EPJK_PF4_RGC_DPG: US jet fuel spot price

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoEnd date (YYYY-MM or YYYY-MM-DD). Default: latest available
startNoStart date (YYYY-MM or YYYY-MM-DD). Default: 2 years ago
lengthNoMax rows to return (API max: 5000). Omit to let date range control volume.
offsetNoRow offset for pagination (use with length)
productNoProduct type: 'crude' (default — WTI), 'gasoline', 'diesel', 'all'. Or a specific series ID like 'EPCWTI'
frequencyNoFrequency (default: monthly)
Behavior2/5

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

The description is consistent with the readOnlyHint=true annotation, but it adds no behavioral context beyond the data categories. It does not mention pagination, frequency defaults, rate limits, or response structure, all of which would be valuable for an agent invoking this tool.

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 description is compact and front-loaded, with a clear opening sentence followed by a scannable product-code list. Every line provides actionable information; there is no redundancy or filler.

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

Completeness3/5

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

The tool has no output schema, and the description does not explain the return format or expected data structure. However, it does enumerate available series and clearly defines the data domain. With 100% schema parameter coverage and the readOnly annotation, the description is serviceable but leaves room for improvement in setting expectations about the response.

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?

The input schema already covers all six parameters, but the description enriches the 'product' parameter by providing specific EIA series IDs and their human-readable meanings (e.g., EPCBRENT, EMM_EPMRU_PTE_NUS_DPG). This goes beyond the schema's generic description and aids correct selection.

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 clearly states the tool's purpose with a specific verb and resource: 'Get petroleum/oil prices' and enumerates exact price categories (crude spot, retail gasoline, diesel, heating oil). This distinguishes it from sibling EIA tools like eia_electricity and eia_natural_gas.

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 implies usage by listing the data available, but it does not explicitly state when to use this tool over alternatives or when not to use it. It lacks exclusion guidance or comparisons with sibling tools, so the context is inferred rather than explicit.

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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