weather_nowCurrent temperature, humidity, wind, rain across Singapore weather stations.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| wind | No | Current wind speed readings by station | |
| humidity | No | Current relative humidity readings by station | |
| rainfall | No | Current rainfall readings by station | |
| temperature | Yes | Current temperature data across weather stations |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "humidity": {
+ "description": "Current relative humidity readings by station",
+ "type": [
+ "object",
+ "null"
+ ]
+ },
+ "rainfall": {
+ "description": "Current rainfall readings by station",
+ "type": [
+ "object",
+ "null"
+ ]
+ },
+ "temperature": {
+ "description": "Current temperature data across weather stations",
+ "type": "object"
+ },
+ "wind": {
+ "description": "Current wind speed readings by station",
+ "type": [
+ "object",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "temperature"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds no further behavioral details (e.g., data freshness or update frequency), but does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short sentence that efficiently communicates the tool's purpose without any wasted words. It is appropriately sized for a simple, parameterless tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, output schema exists), the description covers the key aspects: data types and geographic scope. Though it omits details like data freshness, the presence of an output schema compensates for not needing to describe return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema coverage is 100%. The description adds no parameter-level information, but a baseline of 4 is appropriate since no compensation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides current temperature, humidity, wind, and rain across Singapore weather stations. It uses a specific verb and resource, and the distinction from sibling tools (e.g., air quality, visibility) is clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use or when not to use this tool vs. alternatives. However, the scope (weather data) and sibling names (air_quality, uv_index, etc.) imply appropriate usage context, meeting the 'implied usage' level.
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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