get_weatherReturns current conditions and a 7-day forecast for Sedona AZ from Open-Meteo (free, no key required).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| daily | No | ||
| hourly | No | ||
| current | No | ||
| latitude | No | ||
| timezone | No | ||
| elevation | No | ||
| longitude | No | ||
| daily_units | No | ||
| hourly_units | No | ||
| current_units | No | ||
| generationtime_ms | No | ||
| utc_offset_seconds | No | ||
| timezone_abbreviation | No |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "current": {
+ "additionalProperties": {},
+ "type": "object"
+ },
+ "current_units": {
+ "additionalProperties": {
+ "type": "string"
+ },
+ "type": "object"
+ },
+ "daily": {
+ "additionalProperties": {},
+ "type": "object"
+ },
+ "daily_units": {
+ "additionalProperties": {
+ "type": "string"
+ },
+ "type": "object"
+ },
+ "elevation": {
+ "type": "number"
+ },
+ "generationtime_ms": {
+ "type": "number"
+ },
+ "hourly": {
+ "additionalProperties": {},
+ "type": "object"
+ },
+ "hourly_units": {
+ "additionalProperties": {
+ "type": "string"
+ },
+ "type": "object"
+ },
+ "latitude": {
+ "type": "number"
+ },
+ "longitude": {
+ "type": "number"
+ },
+ "timezone": {
+ "type": "string"
+ },
+ "timezone_abbreviation": {
+ "type": "string"
+ },
+ "utc_offset_seconds": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds context about the free, no-key data source and location-specific behavior (Sedona AZ), which is valuable beyond 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, concise sentence that front-loads the key information: what is returned, location, and data source. No wasted words.
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 zero parameters and an output schema, the description is complete. It clearly defines the scope (Sedona AZ weather) and data source, which is sufficient for the agent to use this tool correctly.
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?
There are zero parameters and schema description coverage is 100%, so the baseline is 3. The description adds output details (current conditions and 7-day forecast), but since output schema exists, the description need not explain return values.
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 returns current conditions and a 7-day forecast for Sedona AZ, specifying the data source (Open-Meteo, free, no key). This distinguishes it from siblings like get_air_quality or get_moon_phase.
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?
The description provides no explicit guidance on when to use this tool versus alternatives (e.g., get_air_quality, get_moon_phase). It only states what it does, leaving the agent to infer usage context.
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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