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LiveDataLink

by io.github.blackboxfoundry

nrel_alt_fuel_stations

Read-onlyIdempotent

Find alternative fuel stations near a location: electric (EV) charging, CNG, LNG, E85, hydrogen, propane, biodiesel. Used by route planning agents, fleet operators, and EV/clean-fuel tech.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude. Use with lon as alternative to location.
lonNoLongitude. Use with lat as alternative to location.
limitNoMax stations to return (default 25, max 200).
stateNoOptional 2-letter state code filter.
radiusNoSearch radius in miles (default 5, max 500).
statusNoOptional status filter: E (available, default), P (planned), T (temporarily unavailable).
locationNoAddress or city/state. Either location OR lat+lon required.
fuel_typeNoComma-separated fuel types: ELEC (default), CNG, LNG, E85, HY, LPG, BD.

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already establish this as a read-only, idempotent, non-destructive operation, lowering the bar. The description adds no new behavioral details such as data freshness, response variability, or open-world caveats beyond what the annotations already imply.

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 two tight sentences: the first states the core function and supported fuel types, and the second gives the intended audience. There is no filler or repetition of schema details.

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?

The schema is rich enough to cover parameter semantics, including the location-or-lat/lon requirement and defaults. The description is adequate for a read-only lookup tool, though it does not mention the relationship to nrel_alt_fuel_station_detail or describe the output shape, which would be helpful since no output schema is present.

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 carries the full documentation for all 8 parameters. The description only previews fuel types and location-based search, adding no meaning beyond the schema for parameter values, defaults, or constraints.

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 states a specific verb and resource: 'Find alternative fuel stations near a location', and enumerates the fuel types covered (EV, CNG, LNG, E85, hydrogen, propane, biodiesel). It does not explicitly differentiate itself from the sibling nrel_alt_fuel_station_detail, though 'near a location' implies a search over a detail 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 gives use-case context ('Used by route planning agents, fleet operators, and EV/clean-fuel tech'), which implies when the tool might be relevant. However, it does not explicitly state when to choose this tool over alternatives or mention the detail sibling for retrieving specific station records.

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