Weather — NOAA Conditions, Forecasts & History for AI Agents

get_weather

Read-onlyIdempotent

Current observed weather conditions for an agricultural region — temperature, humidity, wind, and sky conditions from the nearest NOAA National Weather Service station. Use for right-now conditions; use get_forecast instead for the outlook ahead, and get_weather_history for what the past days actually delivered. Live proxy to NOAA — nothing is stored, so values are as of the station's latest observation; no API key. An unknown location key or a NOAA outage returns a source-unavailable error.

Input Schema

Name	Required	Description	Default
`location`	No	Location key (IL-central, IA-central, NE-central, IN-central, KS-central). Default: IL-central	IL-central

Output Schema

Name	Required	Description	Default
`data`	Yes
`status`	Yes
`provenance`	Yes
`status_detail`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 2, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "anyOf": [
+        {
+          "properties": {
+            "conditions": {
+              "type": [
+                "string",
+                "null"
+              ]
+            },
+            "humidity": {
+              "type": [
+                "number",
+                "null"
+              ]
+            },
+            "location": {
+              "type": "string"
+            },
+            "temp": {
+              "type": [
+                "number",
+                "null"
+              ]
+            },
+            "tempUnit": {
+              "type": "string"
+            },
+            "timestamp": {
+              "type": "string"
+            },
+            "windDirection": {
+              "type": [
+                "number",
+                "null"
+              ]
+            },
+            "windSpeed": {
+              "type": [
+                "number",
+                "null"
+              ]
+            }
+          },
+          "required": [
+            "conditions",
+            "humidity",
+            "location",
+            "temp",
+            "tempUnit",
+            "timestamp",
+            "windDirection",
+            "windSpeed"
+          ],
+          "type": "object"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "provenance": {
+      "properties": {
+        "cadence": {
+          "type": "string"
+        },
+        "dataset": {
+          "type": "string"
+        },
+        "source": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "cadence",
+        "dataset",
+        "source"
+      ],
+      "type": "object"
+    },
+    "status": {
+      "enum": [
+        "ok",
+        "source_unavailable"
+      ],
+      "type": "string"
+    },
+    "status_detail": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "status",
+    "data",
+    "provenance"
+  ],
+  "type": "object"
+}

First observedOct 24, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive/open-world, and the description goes well beyond them: it discloses the upstream source, that it is a live proxy with nothing stored, that no API key is needed, and the exact failure mode (source-unavailable error on unknown location key or NOAA outage). That failure semantics is exactly the kind of operational detail annotations cannot express.

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?

Dense but front-loaded: purpose first, then routing to siblings, then source/behavioral caveats. Every clause (live proxy, nothing stored, no API key, error case) carries information an agent would otherwise have to discover by failing.

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?

An output schema exists, so return-value explanation is unnecessary, and the one optional parameter is fully covered by the schema. Combined with annotations, the description leaves nothing an agent needs in order to call it correctly or interpret an error.

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% and the enum-like location keys are documented in the schema, so the baseline is 3. The description still adds value by framing locations as 'agricultural regions' resolved to the nearest station and by stating that an unknown location key yields a source-unavailable error, which clarifies the parameter's validation behavior.

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?

States a specific verb and resource ('Current observed weather conditions for an agricultural region') with the exact data fields and provenance (nearest NOAA NWS station). It explicitly names the two sibling tools it is not, so an agent can route without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use ('right-now conditions') and pairs each alternative with the condition that selects it: get_forecast for the outlook ahead, get_weather_history for past days. No inference required.

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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