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by io.github.blackboxfoundry

eia_electricity_state

Read-onlyIdempotent

Monthly state-level electricity data from EIA. Filter by state (two-letter code or 'US' for national), sector (residential / commercial / industrial / transportation / all), and metric (price / sales / revenue / customers / generation). Default: US, all sectors, price.

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).
stateNoTwo-letter state code (e.g. 'TX', 'CA') or 'US' for national rollup. Default 'US'.
metricNoMetric: 'price', 'sales', 'revenue', 'customers', 'generation'. Default 'price'.
sectorNoSector: 'all', 'residential', 'commercial', 'industrial', 'transportation'. Default 'all'.

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, and non-destructive behavior, so the safety profile is covered. The description adds the monthly cadence and default filter behavior, but no further disclosure about pagination, response size, or return format is provided.

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 front-loaded sentences with no filler: the resource, filter options, and defaults are conveyed efficiently. 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 read-only, no-required-param tool with a fully documented schema and rich annotations, the description plus schema is enough to call it correctly. It covers cadence, filter dimensions, and defaults, though it does not describe the result shape or explicitly disambiguate from sibling EIA tools.

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 coverage is 100%, so the baseline is met even without additional parameter explanation. The description restates the state, sector, and metric filter values plus defaults without adding new facts beyond the schema, so it neither improves nor harms parameter understanding.

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 ('monthly state-level electricity data from EIA') and its scope, which distinguishes it from EIA siblings focused on gasoline, natural gas, oil, renewables, and consumption. It lacks an explicit retrieval verb like 'get' or 'list', so it does not fully meet the 5 bar.

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 usable context by listing the filter dimensions and defaults, implying this tool is for EIA state-level electricity queries. It does not explicitly name alternatives or state when not to use this tool, leaving some selection work to the agent.

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.

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