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LiveDataLink

by io.github.blackboxfoundry

eia_oil_supply

Read-onlyIdempotent

Weekly US crude oil supply data from EIA. Metrics: 'production' (US field production), 'imports' (weekly oil imports), 'stocks' (commercial crude stocks), 'refinery_inputs' (gross refinery inputs). Filter by PADD region. Default: national production.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoInclusive upper-bound period (ISO date or YYYY-MM).
limitNoMaximum rows to return (default 50, max 5000).
startNoInclusive lower-bound period (ISO date or YYYY-MM depending on series cadence).
metricNoMetric: 'production', 'imports', 'stocks', 'refinery_inputs'. Default 'production'.
regionNoPADD region or 'national'. Examples: 'national', 'PADD1', 'PADD3'. Default 'national'.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (read-only, idempotent, non-destructive), and the description adds value by explaining what each metric means and stating the default scope. It omits return-envelope or pagination details, but the annotation coverage lowers the burden and the added metric semantics are genuinely useful.

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 three sentences: source/cadence, metric catalog, and filtering/default behavior. There is no filler, and the most important scoping information is front-loaded.

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 query with no required parameters, the description supplies what is needed to make a valid first call: default metric/region, cadence, region options, and metric choices. The schema covers start/end/limit formatting; the main minor gap is that no return shape is described, and there is no output schema to compensate.

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?

Schema coverage is 100%, putting the baseline at 3, but the description goes beyond the schema by defining the semantic meaning of each metric (e.g., stocks = commercial crude stocks). It also clarifies the default combination of metric and region, helping the agent choose meaningful parameter values.

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 names the exact resource (weekly US crude oil supply data from EIA) and enumerates the four metric variants, making the tool's scope unmistakable. It differentiates from EIA siblings by focusing on crude oil supply rather than gasoline, natural gas, electricity, or renewables.

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

Usage Guidelines4/5

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

It gives clear context for use: weekly crude oil metrics, PADD region filtering, and a national production default. It does not explicitly name alternative tools or when-not-to-use conditions, so it stops short of a full 5, but an agent can confidently select it for crude oil supply questions.

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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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