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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

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish readOnlyHint, openWorldHint, idempotentHint, and non-destructiveness. The description adds useful behavioral context by specifying returned fields (high/low, conditions, precipitation probability, wind, UV, sunrise/sunset) and the 1-16 day range. It does not contradict 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?

The description is front-loaded with the core action and returned data, followed by concrete usage examples and a parameter range. Every sentence adds value; the example queries are practical rather than 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?

There is no output schema, so the description carries the burden of explaining return values, which it does well by listing the forecast fields. It also covers the main invocation need: location, range of days, and use cases. A small gap is the lack of mention of units or the default of 7 days, though the schema covers the default.

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 already documents location and days well. The description adds that the tool works for "any location worldwide" and supports 1-16 day forecasts, but it does not go much beyond the schema's own parameter descriptions. This matches the baseline for fully self-documenting schemas.

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." It lists the exact data fields returned and clearly distinguishes itself from future-weather vs. current-weather tools by emphasizing "future weather question" and "multi-day." This differentiates it from weather_current and other weather-adjacent siblings.

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 rich usage guidance with many concrete example queries like "what's the forecast this week?" and "7-day forecast for Dallas." It makes the intended context clear, though it does not explicitly name an alternative tool or state when not to use this tool in favor of weather_current or another 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.