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Glama

US Economic, SEC EDGAR & On-Chain Data (x402)

by net.agentfund

US Energy Markets (Crude & Natural Gas)

macro_energy
Read-onlyIdempotent

Latest U.S. energy market data from the Energy Information Administration: WTI crude price, crude oil inventories, and natural gas storage.

Combines three EIA series that usually require separate lookups: the WTI Cushing spot price, weekly U.S. crude oil ending stocks (with week-over-week percent change), and weekly natural gas underground storage (with week-over-week percent change).

When to use: energy-sector context, inflation pass-through analysis (energy prices feed CPI/PCE), trading around the weekly EIA inventory releases.

When NOT to use: you need regional/PADD-level breakdowns, refined product prices (gasoline, diesel), or non-U.S. energy data.

Args: none.

Returns structuredContent: { "asOf": "2026-08-07", "wtiSpotUsdPerBbl": 84.77, "crudeStocksThousandBbl": 420000, "crudeStocksWowPercent": -1.2, "naturalGasStorageBcf": 3100, "naturalGasStorageWowPercent": 0.8, "source": "https://www.eia.gov/petroleum/" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds valuable behavioral context by explaining it combines three EIA series into one lookup and includes week-over-week percent changes, plus the data source. No contradictions with annotations.

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 well-structured with clear sections for overview, usage, exclusions, and return format. Every sentence adds value, and the example output is front-loaded enough for quick understanding without unnecessary verbosity.

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?

Despite having no output schema, the description provides a complete structuredContent example, showing the exact fields and units. It covers data source, frequency, and scope, making the tool's behavior fully understandable without needing additional lookups.

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 tool has zero parameters, and the description explicitly states 'Args: none,' which is appropriate. With no schema to explain, the description correctly notes the absence of arguments, making parameter semantics self-evident.

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 that the tool retrieves U.S. energy market data (WTI crude price, crude oil inventories, natural gas storage) from the EIA. It explicitly lists the three combined series, distinguishing it from siblings like macro_gdp and macro_jobs by naming specific energy metrics.

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

Usage Guidelines5/5

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

The description provides an explicit 'When to use' section naming energy-sector context, inflation pass-through analysis, and trading around EIA releases. It also includes a 'When NOT to use' section excluding regional breakdowns, refined products, and non-U.S. data, giving clear guidance on alternatives.

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

All US Economic, SEC EDGAR & On-Chain Data (x402) toolsInspect
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TDQS

A4.5/5.0
Disambiguation5/5

Every tool targets a distinct resource and action. The macro_* tools each cover one economic indicator, the edgar_* tools cover different SEC filing types, and the onchain_* tools are split by chain scope (single vs multi), asset type, and operation. Even the two data-cleaning tools are clearly distinct (JSON repair vs table parsing). No two tools appear to do the same thing.

Naming Consistency4/5

Names follow a mostly consistent snake_case pattern with domain prefixes: macro_*, edgar_*, onchain_*. The exceptions are bls_cpi (could be macro_cpi) and the utility tools structured_json_repair and tabular_to_json, which break the prefix pattern but are still descriptive and predictable. Overall, the convention is clear with minor deviations.

Tool Count3/5

21 tools is in the 'heavy' range (16-25). However, the server spans three distinct domains (US economic data, SEC EDGAR, on-chain data), and each tool serves a unique purpose within its domain. While it feels dense, the breadth is justified by the server's stated multi-domain scope.

Completeness4/5

The tool surface covers the major needs in each domain: key macro indicators, common EDGAR filings and searches, and core on-chain reads. Minor gaps exist (e.g., no PPI, no historical on-chain balances, no company CIK lookup), but agents can work around these with existing tools or by combining them.

Resources