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by io.github.blackboxfoundry

nrel_pvwatts

Read-onlyIdempotent

Estimate solar PV system production using NREL's PVWatts v8 model. Returns annual and monthly AC energy output (kWh), solar resource (kWh/m²/day), and capacity factor. Used by solar developers, homeowners, and ESG analysts to size and estimate solar arrays.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude in decimal degrees. Use with lon as alternative to address.
lonNoLongitude in decimal degrees. Use with lat as alternative to address.
tiltNoArray tilt angle in degrees (default 20).
lossesNoTotal system losses percent (default 14).
addressNoStreet address, city/state, or place name. Either address OR lat+lon required.
azimuthNoArray azimuth in degrees (default 180 = south for northern hemisphere).
array_typeNo0=fixed open rack, 1=fixed roof (default), 2=1-axis tracking, 3=1-axis backtracking, 4=2-axis tracking.
module_typeNo0=standard (default), 1=premium, 2=thin film.
system_capacityYesSystem size in kilowatts DC (e.g. 5 for a 5 kW residential system).

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful behavioral context beyond annotations by naming the model version and the concrete returned quantities with units (annual/monthly AC kWh, solar resource, capacity factor).

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?

Three tight sentences with no filler: verb and resource first, then outputs, then intended users. Every sentence earns its place and the core behavior 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?

Despite having no output schema, the description compensates by naming the key return values and their units. Combined with a fully described input schema, an agent has enough to invoke it for a solar-estimation task.

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 schema fully documents all 9 parameters. The description adds no parameter-specific semantics, which is acceptable because the schema already carries that burden.

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 has a specific verb ('Estimate'), a specific resource ('NREL's PVWatts v8 model'), and enumerates expected outputs. It is clearly a solar-production model, but it does not explicitly distinguish itself from the related sibling nrel_solar_resource.

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?

It names target users and use cases ('solar developers, homeowners, and ESG analysts to size and estimate solar arrays'), implying when to use it. It does not explicitly state when not to use it or point to an alternative tool.

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