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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds valuable context beyond annotations by specifying the categories of metadata returned (filters, groupings, metrics, detail fields, citation fields), which helps the agent know what to expect without contradicting the 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, front-loaded sentence of about 15 words. Every word is meaningful—it identifies the action, the dataset, and the scope. No filler or redundant phrasing.

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 zero-parameter describe tool with an output schema present, the description fully captures what the tool does and what it returns at a high level. It leaves no ambiguity about the dataset or the categories of information described, making it complete for its simple context.

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, so the baseline is 4. The description correctly focuses on the describe functionality rather than any parameter details, and no additional parameter semantics are 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 states a specific verb ('Describe') and a specific resource ('power.demand'), and enumerates the exact aspects covered (valid filters, groupings, metrics, detail fields, and citation fields). The tool name further distinguishes it from sibling describe tools for other datasets, making purpose unambiguous.

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 implies its usage context—an agent would use this tool to learn the valid query components for power.demand. However, it does not explicitly state when to use this over query_power_demand_v1 or other describe tools, nor does it provide exclusions or alternative guidance.

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