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LiveDataLink

by io.github.blackboxfoundry

sun_times

Read-onlyIdempotent

Compute sunrise, sunset, solar noon, and civil twilight for any latitude/longitude on a given date. Times are computed in code from the standard NOAA solar-position equations (no third-party API is called, so the result is dependency-free and resale-safe). Times are returned in UTC by default; pass tz_offset (hours from UTC, e.g. -7 for US Pacific Daylight Time) to shift the output to local clock time. Polar day and polar night are reported when the sun does not rise or set. Use it for daylight planning, photography golden-hour timing, or agriculture and energy calculations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude in decimal degrees (positive north).
lonYesLongitude in decimal degrees (negative west).
dateNoDate, 'YYYY-MM-DD'. Defaults to today (UTC).
tz_offsetNoHours from UTC applied to output times, e.g. -7 for US Pacific Daylight Time. Default 0 (UTC).

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already mark the tool read-only/idempotent, and the description adds useful non-obvious behavior: computation is local via NOAA equations (no third-party API), outputs default to UTC with a tz_offset shift option, and polar day/night are represented explicitly. No contradiction with annotations.

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?

Four sentences, each earning its place: purpose, implementation detail, timezone behavior, edge cases, and use cases. The main action is front-loaded and there is no 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 stateless calculation with a complete schema, it covers inputs, defaults, timezone behavior, edge cases, and use cases. It stops short of specifying the exact return object/field names, which would matter more if an output schema were absent; still, enough information is present for correct selection and invocation.

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 every parameter with defaults and examples. The description restates the tz_offset behavior rather than adding new parameter semantics; the baseline of 3 is appropriate.

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 opens with a specific verb ('Compute') and exact resource ('sunrise, sunset, solar noon, and civil twilight') plus the required scope (latitude/longitude/date). This clearly differentiates it from geospatial or weather siblings such as tide_predictions or 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit application contexts: daylight planning, photography golden-hour timing, agriculture, and energy calculations. It does not name alternatives or exclusions, but among the sibling list there is no direct equivalent, so the use-case guidance is sufficient for selection.

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