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

Annotations already mark the tool read-only, idempotent, non-destructive, and open-world. The description goes well beyond that by disclosing data recency ('strictly at-or-before now'), the 30-day ranking window, sample-mode truncation behavior, authentication methods, and the fact that sample results are clearly marked. This is rich behavioral context with no contradiction.

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 longer than average, but nearly every sentence earns its place: core output, use-case guidance, authentication options, and sample-mode caveats. It is front-loaded with the tool's purpose and distinctive value, though the authentication and sample-mode section could be trimmed slightly without losing critical information.

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?

With no output schema, the description fully compensates by enumerating the return components: current metrics, percentile/median/min/max ranking, sample count and data window, 24h trend, generation mix where published, and a plain-English summary. It also covers authentication, rate limits, sample-mode truncation, and marked sample results, making the tool safely invocable by an agent.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful value by explaining the auth semantics of api_key: prefer an Authorization header, use the parameter only when headers cannot be set, and behavior without a key (free sample mode with truncated output). It also clarifies that zone supports specific regions and that list_zones provides more metadata.

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 opens with a specific verb-plus-resource construction: 'Composite, interpretation-ready snapshot of one zone,' listing exact metrics (price, demand, carbon intensity), ranking context, 24h trend, generation mix, and summary. It clearly differentiates itself from raw data tools by saying 'A raw price means little without this context' and positioning itself as the best tool for current-state questions.

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 explicitly states when to use the tool: 'Best tool for questions like "is electricity cheap/clean in X right now" or "is this a good time to run a flexible workload".' It provides clear context for selection, though it does not explicitly name sibling alternatives or state when not to use the tool, leaving some differentiation to inference.

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