get_weather_forecast

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

With no annotations, the description carries the behavioral burden; it is transparent about the output shape, stating 'JSON with forecast, AI analysis, and ambient theme', and the read-only nature is implied by 'Get'. However, it does not disclose external API dependencies, possible latency from AI analysis, failure modes, or caching behavior.

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 compactly organized into three scannable sections: summary, Args, and Returns. Every sentence or line contributes either parameter semantics or output expectations, with no filler or repeated schema trivia.

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?

For a simple read-only forecast tool, the description covers the full calling contract: input parameters, defaults, allowed values, and returned content. The only meaningful gap is the lack of routeing context against the sibling tools and no explanation of what 'ambient theme' means, though an output schema likely covers that.

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 input schema has 0% description coverage, but the Args block compensates fully: location_name gets concrete examples ('Prague', 'London'), days gets a range and default (1-16, default 7), and language gets allowed values (en, cs). This is exactly the semantic detail an agent needs beyond the schema's bare types and defaults.

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 uses a specific verb and resource: 'Get weather forecast', and adds a clear differentiator with 'AI-powered analysis' and the return of an 'ambient theme'. This separates it from siblings like get_current_weather, get_weather_by_coordinates, and get_ambient_theme, even without naming them explicitly.

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?

There is no guidance about when to prefer this tool over the five sibling tools. It does not mention alternatives for current conditions, coordinate-based lookup, or dedicated ambient-theme retrieval, so an agent must infer the intended use case from the tool name and description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.