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Tomorrow Io

by io.github.pipeworx-io
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Forecast

forecast
Read-onlyIdempotent

Get a daily or hourly weather forecast for a location. Returns a list of forecast periods with conditions, temperatures, precipitation probability, and wind. Location can be "lat,lon" coordinates or a city name. Example: forecast({ location: "london", timestep: "1d", units: "metric" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
unitsNoUnit system: "metric" (default) or "imperial"
_apiKeyNoTomorrow.io API key (optional — get a free one at tomorrow.io; if omitted the platform key is used)
locationYesLocation to look up — either "lat,lon" coordinates (e.g. "40.71,-74.01") or a city name (e.g. "new york")
timestepNoForecast granularity: "1d" daily (default) or "1h" hourly

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "location": "london",
+    "timestep": "1d",
+    "units": "metric"
+  },
+  {
+    "_apiKey": "your-tomorrow-io-api-key",
+    "location": "40.71,-74.01",
+    "timestep": "1h",
+    "units": "imperial"
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Description discloses return format (list of periods with conditions, temperatures, precipitation, wind) and input options (coordinates or city name). Annotations confirm read-only, idempotent, non-destructive behavior, and description does not contradict.

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?

Three sentences: first defines purpose, second explains return, third gives example. No unnecessary words. Information is 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?

Schema and annotations are rich; output schema is absent but description covers return content adequately. For a simple forecast tool, this is sufficient. Minor gap: no mention of error cases or rate limits.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. Description adds value by providing an example call with parameter interplay and explaining location formats. The example clarifies usage beyond schema descriptions.

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 'Get a daily or hourly weather forecast for a location.' It specifies the verb (get), resource (forecast), and granularity options (daily/hourly). Among sibling tools, no other weather forecast tool exists, making it distinct.

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 explains location formats and provides an example call. While it doesn't explicitly state when not to use, it is the only weather forecast tool, so usage context is clear. A minor gap is the lack of alternative recommendations.

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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