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Glama

Weatherapi

by io.github.pipeworx-io

Forecast

forecast
Read-onlyIdempotent

Get a multi-day weather forecast for a location: daily high/low temperature, condition, chance of rain, total precipitation, and sunrise/sunset times. Up to 10 days. Example: forecast({ q: "Tokyo", days: 5 }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesLocation — city name (e.g. "London"), "lat,lon" (e.g. "48.8567,2.3508"), US/UK/Canada zip/postcode, IATA airport code (e.g. "DXB"), or "auto:ip". NOTE on IATA codes: WeatherAPI resolves them to that airport's CITY, not the airport's own weather station — for the actual airport-station reading (can differ by several degrees) use the aviation-weather pack's metar tool with the airport's ICAO code instead.
daysNoNumber of forecast days (default 3, max 10).
_apiKeyNoOptional — your own WeatherAPI.com 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
    • changedInput schema / properties / q / description
      Previous value: -"Location — city name (e.g. \"London\"), \"lat,lon\" (e.g. \"48.8567,2.3508\"), US/UK/Canada zip/postcode, IATA airport code (e.g. \"DXB\"), or \"auto:ip\"."New value: +"Location — city name (e.g. \"London\"), \"lat,lon\" (e.g. \"48.8567,2.3508\"), US/UK/Canada zip/postcode, IATA airport code (e.g. \"DXB\"), or \"auto:ip\". NOTE on IATA codes: WeatherAPI resolves them to that airport's CITY, not the airport's own weather station — for the actual airport-station reading (can differ by several degrees) use the aviation-weather pack's metar tool with the airport's ICAO code instead."
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "days": 5,
+    "q": "Tokyo"
+  }
+]
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds what data is returned and the 10-day limit. It does not mention rate limits or API key nuances, but the key behavioral context is present. 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?

Two concise sentences, front-loaded with purpose, followed by a relevant example. Every word earns its place; no filler or redundancy.

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?

With no output schema, the description appropriately lists return fields (high/low, condition, rain chance, precipitation, sunrise/sunset) and the 10-day maximum. Combined with rich schema and annotations, the tool is fully understandable for a read-only forecast operation.

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%, with detailed descriptions for all three parameters. The main description adds no extra parameter semantics beyond the example call, which merely mirrors the schema example. Baseline 3 is appropriate since schema does the heavy lifting.

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 clearly states 'Get a multi-day weather forecast for a location' and lists specific data (daily high/low, condition, chance of rain, precipitation, sunrise/sunset). This distinguishes it from sibling tools like 'current' or 'astronomy' by focusing on multi-day forecast details.

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 description implies usage for multi-day forecasts via the example and the word 'multi-day', but it does not explicitly contrast with sibling tools (e.g., 'for current conditions use current') or state exclusions. No when-not-to-use guidance is given.

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

A3.8/5.0
Disambiguation2/5

Several tools have near-identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical, and six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, polymarket_edge_tracker) overlap heavily in discovery, edge, and arbitrage roles. Company-research tools (entity_profile, compare_entities, recent_changes) also blur boundaries, making misselection likely.

Naming Consistency3/5

All names use lowercase snake_case with underscores, which is a consistent base convention. However, the lexical pattern varies: bare single words (current, forecast, remember, forget) coexist with verb_noun compounds (resolve_entity, validate_claim) and noun compounds (entity_profile, polymarket_edges). The lack of a uniform verb_noun structure makes the set less predictable, though still readable.

Tool Count1/5

35 tools is well into the 'too many' range, and the server's name promises weather while only 4 of 35 tools (current, forecast, astronomy, marine) are weather-related — an extreme mismatch between the declared purpose and the actual surface. The remaining 31 tools form a general data/prediction-market platform that would be better served under a different server name.

Completeness4/5

Judged by its actual (non-weather) domain, the set is quite complete: generic routed lookup, grounded answer mode, deep research, entity resolution/profile/comparison, claim validation, subscriptions, memory, discovery, and feedback are all present. The weather subset covers current conditions, forecasts, marine, and astronomy, though it lacks historical weather and alert endpoints — a minor gap.

Resources