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LiveDataLink

by io.github.blackboxfoundry

weather_forecast

Read-onlyIdempotent

Get a multi-day weather forecast for any location worldwide. Returns daily high/low temperatures, conditions, precipitation probability, wind speed, UV index, sunrise and sunset. Use this for 'what's the forecast this week?', 'will it rain tomorrow?', 'weekend weather', 'should I plan outdoor activities?', '7-day forecast for Dallas', or any future weather question. Supports 1-16 day forecasts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoForecast days (default: 7)
locationYesCity, zip code, or place name

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, covering the safety profile. The description adds useful context by enumerating the returned data (high/low, conditions, precipitation probability, wind speed, UV index, sunrise/sunset) and the 1–16 day range, setting expectations beyond the structured fields. It does not mention units or error behavior, but those gaps are minor.

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 front-loaded with the core purpose, then the return values, then usage examples, then the supported range. Each sentence adds distinct value—purpose, output, trigger language, constraints—and there is no filler or tautology.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter, read-only forecast tool, the description is complete for selection and invocation: it names the resource, lists the return fields, covers the supported day range, and gives natural-language triggers. The schema handles parameter details, so no output schema is needed for basic usage.

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%: both 'location' and 'days' are already documented, including the default of 7 and the min/max range. The description mostly restates the 1–16 day range and worldwide scope, adding no new parameter-level meaning, so the baseline 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 and resource—'Get a multi-day weather forecast for any location worldwide'—and clearly differentiates from current-weather tools by emphasizing 'multi-day' and 'future weather.' The return fields and examples make the tool's purpose unmistakable.

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?

The description gives explicit usage examples ('will it rain tomorrow?', 'weekend weather', '7-day forecast for Dallas') and states 'any future weather question,' providing clear context for when to invoke it. However, it never explicitly contrasts it with the sibling weather_current, so it lacks a formal when-not-to-use statement.

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.

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