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LiveDataLink

by io.github.blackboxfoundry

nrel_solar_resource

Read-onlyIdempotent

Annual and monthly solar resource data (Direct Normal Irradiance, Global Horizontal Irradiance, Latitude-Tilt Irradiance) for a location. Useful for site evaluation before sizing a solar system.

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.
addressNoStreet address, city/state, or place name. Either address OR lat+lon required.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already mark the operation read-only, idempotent, and non-destructive. The description adds useful behavioral detail by specifying the time granularity (annual and monthly) and the three irradiance output types, which goes beyond what the annotations provide.

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 sentences with no filler: the first delivers the resource and data content, and the second adds the intended use case. 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 simple read-only data lookup, the description covers what is returned, the granularity, and the practical context. It could be more complete by naming units or response shape, but none of the selection-critical information is missing.

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%, with lat, lon, and address each already documented, including the either address OR lat+lon requirement. The description itself adds no parameter-level meaning, so the baseline 3 applies.

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 identifies a specific resource: solar resource data for a location, and enumerates the exact irradiance metrics (DNI, GHI, latitude-tilt). It stops short of a 5 because it does not explicitly distinguish itself from the closely related nrel_pvwatts sibling or state a verb like 'retrieve'.

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 a clear use case: 'site evaluation before sizing a solar system.' This tells an agent when the tool is relevant, but it doesn't explicitly discuss when to prefer nrel_pvwatts or another solar-related sibling.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.

Resources