dashboard_get_consumption_forecast

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

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds value by specifying the data source (Energiedashboard.ch) and the exact forecast types and comparison window, which clarifies the scope and nature of the output.

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?

Two sentences, no wasted words. Key information is front-loaded: what the tool does, specific outputs, source. Highly efficient.

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 existence of an output schema (not shown), description does not need to explain return values. However, it lacks parameter semantics and usage guidelines, making it only minimally complete for a tool with two parameters and sibling tools.

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?

Schema coverage is 0% and description does not mention the two parameters (limit_days, response_format) or their meaning. The agent cannot infer what limit_days controls (e.g., number of historical days? forecast horizon?) and how it relates to the 5-day-ahead forecast mentioned.

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?

Description clearly states verb 'get', resource 'current Swiss national electricity consumption forecast', and lists specific output components (today's value, 5-day-ahead forecast, trend signal, 5-year-window comparison series). It distinguishes from sibling tools like dashboard_get_production_mix by specifying the domain (consumption forecast).

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 this tool vs alternatives. The sibling tools are listed but the description does not mention them or provide usage context. An agent would need to infer from names alone.

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