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Glama

Visualcrossing

by io.github.pipeworx-io

Forecast

forecast
Read-onlyIdempotent

Get the upcoming 15-day daily weather FORECAST for a location — per-day temp/min/max, humidity, precipitation chance, wind, and conditions. Example: forecast({ location: "Berlin" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
unitsNoUnit group: "metric" (default), "us", or "uk".
_apiKeyNoOptional — your own Visual Crossing API key for higher limits; omit to use the shared Pipeworx key.
locationYesCity name (e.g. "Berlin") or "lat,lon".

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: +[
+  {
+    "location": "Berlin"
+  },
+  {
+    "location": "Sydney",
+    "units": "metric"
+  }
+]
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds the 15-day period and fields returned, but does not disclose rate limits, dependency on Visual Crossing API, or any behavioral nuances beyond 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: first defines purpose and output, second provides a clear example. No unnecessary words, well front-loaded.

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?

With no output schema, the description lists key return fields. The example clarifies parameter usage. Lacks mention of the data source (Visual Crossing) but overall sufficient for a simple read tool.

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%; all parameters have descriptions. Description gives a usage example but adds minimal extra meaning beyond schema. 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?

Clearly states it gets a 15-day weather forecast for a location, listing specific fields. Distinguishes from sibling tools like current_conditions and weather_timeline by specifying the 15-day daily scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs alternatives such as current_conditions or weather_timeline. Lacks prerequisites or usage context beyond the example.

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

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose. Even tools with overlapping domains (e.g., ask_pipeworx and deep_research) are differentiated by use case: single lookups vs multi-faceted research. Weather, Polymarket, memory, and subscription tools are completely separate, and descriptions clarify any potential confusion.

Naming Consistency4/5

Tool names mostly follow a verb_noun pattern, but some are single verbs (forget, recall) or noun_noun (entity_profile, weather_timeline). The mix is noticeable but still predictable and readable, with consistent snake_case formatting throughout.

Tool Count4/5

With 34 tools, the server is large but each tool serves a specific function within the broad data-access domain. The count is justified given the wide range of domains (weather, company data, prediction markets, memory, subscriptions, etc.), though it is on the higher end for typical MCP servers.

Completeness4/5

The tool surface covers a wide range of operations: data retrieval, comparison, fact-checking, weather, prediction markets, memory, subscriptions, and meta-tools. There are no obvious gaps for the intended use of a unified data gateway, though some niche data sources might not be directly addressed.

Resources