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Glama

Openweather

by io.github.pipeworx-io

Forecast

forecast
Read-onlyIdempotent

Get a multi-step weather forecast (3-hour intervals) for a city or coordinates: temperature, conditions, wind speed, and precipitation probability over time. Example: forecast({ city: "Tokyo", count: 8 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude. Use with lon instead of city.
lonNoLongitude. Use with lat instead of city.
cityNoCity name, optionally with country code, e.g. "Tokyo" or "Tokyo,JP". Provide this OR lat/lon.
countNoNumber of 3-hour forecast steps to return (default 8 = next 24h, max 40 = 5 days).
unitsNoTemperature units: "metric" (C, default), "imperial" (F), or "standard" (K).
_apiKeyNoOptional — your own OpenWeatherMap API key for higher limits; omit to use the shared Pipeworx key.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-openweather-api-key",
+    "city": "Tokyo,JP",
+    "count": 8
+  },
+  {
+    "_apiKey": "your-openweather-api-key",
+    "count": 40,
+    "lat": 35.6762,
+    "lon": 139.6503,
+    "units": "metric"
+  }
+]
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which align with the description. The description adds data content details (fields returned) but does not reveal additional behavioral traits such as rate limits, caching, or external API dependencies. 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 two focused sentences plus a concise example. It is front-loaded with the main purpose and data fields. No unnecessary words or repetition.

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?

The description covers the tool's function, inputs, and output fields. With 6 parameters, no output schema, and full schema coverage, the description provides adequate context. However, it could include a brief note on output format (e.g., JSON structure) to improve completeness, though it remains effective.

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 all parameters. The description only adds an example with 'city' and 'count' but no new semantic meaning beyond what the schema provides. Baseline score 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 specifies a unique action ('Get a multi-step weather forecast'), a distinct resource ('3-hour intervals for a city or coordinates'), and lists returned data fields (temperature, conditions, wind speed, precipitation probability). The example and mention of intervals clearly differentiate it from sibling tool 'current_weather'.

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?

The context (forecast vs. current) is implied but not explicitly stated. There is no 'when to use' or 'when not to use' guidance, nor mention of alternatives like current_weather. The example shows usage but does not clarify when to choose this tool over siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Openweather toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions. However, 'ask_pipeworx' and 'ask_pipeworx_grounded' are very similar, and 'polymarket_arbitrage' and 'polymarket_edges' overlap in scope, which could cause confusion for an agent.

Naming Consistency3/5

Tool names mostly use snake_case but mix verb-first (e.g., 'compare_entities', 'resolve_entity') and noun-first (e.g., 'air_quality', 'entity_profile') patterns. Some names are single words ('forecast', 'geocode'), showing overall inconsistency.

Tool Count3/5

With 30 tools, the server covers many domains (weather, company info, betting, memory, subscriptions). While each tool serves a purpose, the count is on the higher side and could be streamlined, but it's not excessive given the diverse functionality.

Completeness4/5

The tool set provides comprehensive coverage for its domains: weather (current, forecast, air quality), company research (profile, compare, recent changes, validation), betting (research, arbitrage, edges), memory (CRUD), and subscriptions (CRUD). Minor gaps include no tool for editing subscriptions or deleting weather data, but these are outside the intended scope.

Resources