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LiveDataLink

by io.github.blackboxfoundry

eia_renewable_generation

Read-onlyIdempotent

Monthly US electricity generation by source from EIA. Sources: solar, wind, hydro, nuclear, geothermal, biomass, all. Optional state filter (default national). Returns generation in megawatt-hours.

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 or 'US' for national rollup. Default 'US'.
sourceNoGeneration source: 'all', 'solar', 'wind', 'hydro', 'nuclear', 'geothermal', 'biomass'. Default 'all'.

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare this a safe, read-only, idempotent operation, and the description adds useful behavior beyond them: it states the return unit (megawatt-hours), the default national scope, and the monthly cadence. No contradictions with annotations were found.

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, front-loaded with the core resource, and every sentence adds information: data source, cadence, available sources, filtering, and units. There is no redundant filler.

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 query with no required parameters and a fully documented schema, the description supplies the key missing context: output unit and default national scope. It does not discuss limit behavior or date edge cases, but those are adequately covered by parameter descriptions.

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 adds only a high-level restatement of source and state filtering, which is helpful but not meaningfully beyond the parameter descriptions.

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 a specific resource—monthly US electricity generation from EIA—with an explicit source list and output unit (megawatt-hours). This clearly distinguishes it from sibling EIA tools that cover consumption, prices, or generic series lookup.

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 conveys a clear use case—electricity generation by source with optional state filtering—but it never names alternatives or states when not to use this tool. Selection is implied by the data described rather than explicitly routed among siblings.

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