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

Annotations already mark this as read-only, idempotent, and non-destructive, and the description adds substantial behavioral detail beyond that: Plutus does not compute recommendations itself, commitment rows represent purchases rather than instance swaps, only one term/payment/lookback variant is surfaced, and currency handling differs between individual recommendations and the total. These are exactly the non-obvious behaviors an agent needs to know.

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 dense but every sentence earns its place: purpose, type taxonomy, commitment-row distinction, single-variant caveat, API mapping, and currency behavior. It is front-loaded with the core action and then layers caveats in a logical order, with no filler.

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 zero parameters and no output schema, the description carries the full burden of explaining what the agent will receive. It covers the recommendation types, the special shape of commitment rows, the one-variant limitation, and the currency semantics. Nothing essential is missing for an agent to invoke and interpret 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 no input parameters, so the description has no parameter semantics to explain; baseline 4 applies. It does add useful semantics about the returned `type` enum and the contents of `detail`, which helps with output interpretation even though no output schema is provided.

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 and resource: list active cost-reduction recommendations for this account. It clearly distinguishes the tool from siblings by stating these recommendations come from each provider's own engine (AWS Cost Explorer, Azure Advisor, GCP Recommender), not from Plutus itself. The detailed `type` taxonomy further removes ambiguity about what the tool returns.

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 gives strong contextual guidance for interpreting results, especially the caveat that only one commitment variant is surfaced and should not be treated as exhaustive. It does not explicitly name alternative sibling tools or state when not to use this tool, but the specificity of the semantics effectively routes an agent to this endpoint for provider-computed savings recommendations.

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