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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

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
    • changedInput schema / examples
      Previous value: -[
-  {
-    "location": "london",
-    "timestep": "1d",
-    "units": "metric"
-  },
-  {
-    "_apiKey": "your-tomorrow-io-api-key",
-    "location": "40.71,-74.01",
-    "timestep": "1h",
-    "units": "imperial"
-  }
-]New value: +[
+  {
+    "location": "london",
+    "timestep": "1d",
+    "units": "metric"
+  },
+  {
+    "location": "40.71,-74.01",
+    "timestep": "1h",
+    "units": "imperial"
+  }
+]
  2. 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"
+  }
+]
  3. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior, so the description needn't restate safety. It adds genuine value by disclosing the returned payload (periods with conditions, temperatures, precipitation probability, wind), which is not in the annotations and helps the agent decide whether the output fits its need.

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 sentence order is well front-loaded: purpose first, then return shape, then location format, then a concrete example. It is slightly redundant with the schema's own examples, but no sentence is wasted.

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 carries the return-value burden and does so by enumerating the forecast fields. Combined with full schema coverage and strong annotations, the definition is complete enough for correct invocation, though a note on units defaults or rate limits would close the remaining gap.

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 description coverage is 100%, so the schema already documents location formats, units, timestep, and the optional API key. The description's 'lat,lon or city name' note and example call merely restate schema content; baseline 3 applies since the description adds no syntax or default-override detail beyond the structured fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Get a daily or hourly weather forecast for a location') and names the granularity options. It does not explicitly distinguish itself from nearby siblings such as 'realtime' (likely current conditions) or 'kalshi_weather_edge', so the agent must infer the boundary.

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?

Usage is only implied: 'daily or hourly forecast' suggests consulting this for future conditions, but the description never says when to prefer it over 'realtime' or weather-market tools, nor does it state any prerequisites (e.g., API key fallback) that affect invocation.

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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