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Visualcrossing

by io.github.pipeworx-io

Current Conditions

current_conditions
Read-onlyIdempotent

Get the live CURRENT weather conditions right now for a location — temperature, feels-like, humidity, wind, and conditions. Example: current_conditions({ location: "Tokyo" }).

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. "Tokyo") 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": "Tokyo"
+  },
+  {
+    "location": "New York, NY",
+    "units": "us"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by specifying the exact data fields returned (temperature, feels-like, etc.), providing context beyond the structured 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 a single, clear sentence followed by an example. Every word is purposeful, no fluff. Front-loaded with the core purpose.

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?

Given the simple nature of the tool (read-only, clear parameters, no output schema needed), the description is complete. It explains what the tool does, shows an example, and annotations cover safety. No gaps for an AI agent.

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?

Input schema covers all 3 parameters with descriptions (100% coverage). The description includes an example usage but does not add significant meaning beyond what the schema already 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?

Description clearly states it retrieves live current weather conditions for a location, listing specific data points (temperature, feels-like, etc.) and includes an example. The name 'current_conditions' and sibling 'forecast' imply distinct purpose, differentiating from forecast tools.

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?

Usage is implied by the name and description (for current conditions), and sibling list provides context, but there is no explicit statement on when to use this versus alternatives like 'forecast' or 'weather_timeline'.

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