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Smarter Weather

by io.github.smarterweather
OverviewDiscussionsAdmin

Get current conditions

get_current_conditions
Read-onlyIdempotent

Current weather right now at a location from two independent sources in one call: the RTMA gridded analysis (exact-point values, updated sub-hourly) and the nearest METAR station observation (ground truth with raw METAR, flight category). Use the analysis for point-accurate values and the station for verification. Analysis fields: temperature_2m, dew_point_2m, relative_humidity_2m (derived here from temperature and dew point; listed in analysis.derived), wind_speed_10m, wind_direction_10m, wind_gusts_10m, surface_pressure (station pressure at ground level, not sea-level pressure), visibility, cloud_cover, cloud_ceiling. Analysis values are SI by default; pass units "imperial" or "metric" to convert them (labels in analysis.unit_labels; get_forecast defaults to imperial). nearest_station is the station's own report, unconverted. For a forecast, use get_forecast. Example: {"location": "Pella, IA"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude in decimal degrees (-90 to 90). Most tools also accept a `location` place-name string instead of lat/lon.
lonNoLongitude in decimal degrees (-180 to 180). For continental US use negative values (west of the prime meridian).
unitsNoUnit system for the analysis values: si (default; K, m/s, Pa, m, as RTMA serves them), imperial (°F, mph, inHg, mi, ft) or metric (°C, km/h, hPa, km, m). Unlike get_forecast, which defaults to imperial. Does not apply to nearest_station.si
locationNoFree-text place: city ("Denver"), city+state ("Portland, OR"), US ZIP ("50219"), or "lat,lon" ("39.74,-104.99"). Provide either this OR explicit lat+lon, not both.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
unitsYes
analysisYes
locationYes
nearest_stationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / units
      Added value: +{
+  "default": "si",
+  "description": "Unit system for the analysis values: si (default; K, m/s, Pa, m, as RTMA serves them), imperial (°F, mph, inHg, mi, ft) or metric (°C, km/h, hPa, km, m). Unlike get_forecast, which defaults to imperial. Does not apply to nearest_station.",
+  "enum": [
+    "imperial",
+    "metric",
+    "si"
+  ],
+  "type": "string"
+}
    • changedOutput schema / properties / analysis / anyOf
      Previous value: -[
-  {
-    "additionalProperties": false,
-    "properties": {
-      "data": {
-        "additionalProperties": {
-          "items": {
-            "type": [
-              "number",
-              "null"
-            ]
-          },
-          "type": "array"
-        },
-        "type": "object"
-      },
-      "dataset_id": {
-        "type": "string"
-      },
-      "valid_times": {
-        "items": {
-          "type": "string"
-        },
-        "type": "array"
-      }
-    },
-    "required": [
-      "dataset_id",
-      "valid_times",
-      "data"
-    ],
-    "type": "object"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "additionalProperties": false,
+    "properties": {
+      "data": {
+        "additionalProperties": {
+          "items": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "type": "array"
+        },
+        "type": "object"
+      },
+      "dataset_id": {
+        "type": "string"
+      },
+      "derived": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "notes": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "reference_time": {
+        "type": "string"
+      },
+      "reference_times": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "unit_labels": {
+        "additionalProperties": {
+          "type": "string"
+        },
+        "type": "object"
+      },
+      "valid_times": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      }
+    },
+    "required": [
+      "dataset_id",
+      "valid_times",
+      "data"
+    ],
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedOutput schema / properties / units
      Added value: +{
+  "enum": [
+    "imperial",
+    "metric",
+    "si"
+  ],
+  "type": "string"
+}
    • changedOutput schema / required
      Previous value: -[
-  "location",
-  "analysis",
-  "nearest_station"
-]New value: +[
+  "location",
+  "units",
+  "analysis",
+  "nearest_station"
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations cover read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds valuable context beyond annotations: sub-hourly updates, that RH is derived, that surface_pressure is station-level not sea-level, and unit conversion behavior. It doesn't mention rate limits or auth, but for a read-only weather tool that's minor.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is front-loaded and starts strong, but it becomes a dense wall of field names and unit details that could be streamlined since the schema already covers parameters. It's informative but verges on over-specification, and the example is helpful but placed at the end.

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's complexity (dual data sources, many fields, unit handling) and the presence of an output schema, the description provides enough context to invoke it correctly. It covers sources, fields, units, and alternatives. It doesn't explain the output schema's structure, but that's acceptable since the output schema exists.

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 lat, lon, units, and location thoroughly. The description adds a small clarification about default units (si) and the distinction from get_forecast's imperial default, but mostly repeats schema content. 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?

The description opens with a specific verb+resource ('Current weather right now at a location') and then distinguishes itself from siblings by naming the two data sources (RTMA analysis, nearest METAR station) and explicitly routing forecasts to get_forecast. This level of specificity lets an agent select it correctly without opening the schema.

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?

It gives clear guidance on when to use analysis vs station ('Use the analysis for point-accurate values and the station for verification') and explicitly names get_forecast as the alternative for forecasts. It doesn't mention siblings like get_observations or get_hourly_forecast, which could be ambiguous, so it falls short of a 5.

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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