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Best Window for an Outdoor Activity

weather_best_window
Read-onlyIdempotent

Find the best contiguous N-day or N-hour window in the forecast horizon for an activity (outdoor_event, ski, construction, agriculture_spray, running). Returns top 5 windows ranked 0-100 with drivers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNo
lonNo
top_nNo
horizonNodays
activityNooutdoor_event
zip_codeNo
window_lengthNo
horizon_lengthNo0 = activity-appropriate default (14 days / 48 hours).

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already establish a read-only, idempotent, non-destructive operation. The description adds the useful behavioral claims of contiguous windows and ranked 'top 5' output with drivers, but the 'top 5' claim is inconsistent with the configurable top_n parameter (default 5, max 10), and it does not explain ranking methodology or what drivers are.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the main function before return details. The activity list is somewhat redundant with the schema enum but kept compact; no filler.

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

Completeness2/5

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

For a tool with 8 parameters, no output schema, and no required fields, the description omits location resolution and defaults beyond the implied N-day/N-hour choice; the top_n inconsistency and undefined 'drivers' leave an agent guessing about output shape. It is not complete enough for reliable 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?

With only 13% schema description coverage, the description carries most of the burden; it maps activity to the activity enum, 'N-day or N-hour' to horizon/window_length, and 'top 5' to top_n. However, it does not clarify location parameters (lat/lon vs zip_code, precedence or requirement) or the behavior of window_length/top_n, leaving key parameters under-specified.

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?

States a concrete operation ('Find the best contiguous N-day or N-hour window in the forecast horizon') on a specific resource (an activity-specific weather ranking), enumerates five activity types, and promises a ranked 0-100 output with drivers. This clearly distinguishes it from sibling weather forecast/compare tools.

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 communicates the core use case but gives no explicit when-to-use or when-not-to-use guidance, and it never names alternatives such as weather_forecast, weather_compare, or weather_route. The intended selection is left to inference.

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

Multiple tools have genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.

Naming Consistency4/5

All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).

Tool Count1/5

129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.

Completeness4/5

Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.

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