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Visualcrossing

by io.github.pipeworx-io

Weather Timeline

weather_timeline
Read-onlyIdempotent

Get daily weather for a location — works for BOTH historical weather (past dates) and forecast (future or no dates). Use this for HISTORICAL weather and "weather on a past date" questions, e.g. "what was the weather in Paris on 2023-07-04" (location: "Paris", start_date: "2023-07-04"). Pass start_date alone for a single day, or start_date + end_date for a range (weather timeline). Returns per-day temp/min/max, humidity, precipitation, wind, and conditions. Example: weather_timeline({ location: "London", start_date: "2024-01-01", end_date: "2024-01-07" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
unitsNoUnit group: "metric" (°C, km/h — default), "us" (°F, mph), or "uk".
_apiKeyNoOptional — your own Visual Crossing API key for higher limits; omit to use the shared Pipeworx key.
end_dateNoOptional end date in YYYY-MM-DD format. Only used when start_date is also given; produces a date-range timeline.
locationYesCity name (e.g. "Paris", "New York, NY") or "lat,lon" (e.g. "48.8566,2.3522").
start_dateNoOptional start date in YYYY-MM-DD format. Past dates return HISTORICAL weather; future dates return forecast. Omit for the default 15-day forecast.

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": "Paris",
+    "start_date": "2023-07-04"
+  },
+  {
+    "end_date": "2024-01-07",
+    "location": "48.8566,2.3522",
+    "start_date": "2024-01-01",
+    "units": "metric"
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and no destructive action. The description adds valuable behavioral context: it specifies that it returns daily temperature (min/max), humidity, precipitation, wind, and conditions. It also explains the behavior of start_date alone vs. with end_date, and the default 15-day forecast when no date is given. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well structured, starting with the core purpose, then usage guidelines, and ending with an example. It is moderately concise but includes necessary details. Every sentence contributes value, though it could be slightly streamlined without losing clarity.

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?

Given the tool has 5 parameters (1 required) and no output schema, the description adequately covers input semantics, return fields (temp, humidity, etc.), and usage context. It does not detail the exact output structure, but the list of returned metrics is sufficient for an agent to understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. However, the description adds significant meaning beyond the schema: it explains that start_date alone gives a single day, start_date + end_date gives a range, omitting start_date gives a default 15-day forecast, and past vs. future date behavior. It also provides concrete examples with parameter values, which helps in understanding usage.

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 daily weather for a location' and explicitly covers both historical and forecast use cases. It distinguishes itself from the sibling tool 'forecast' by emphasizing its dual-purpose nature and providing specific examples for past dates.

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 explicitly tells when to use this tool: for historical weather and past dates, as well as for future forecast. It gives concrete examples. However, it does not explicitly mention when not to use it or name alternative tools like 'current_conditions' for immediate weather.

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.

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