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

Annotations already declare this to be a non-destructive, idempotent, closed-world read, so the description isn't carrying the safety burden. It still adds real context beyond the annotations: filtering to "active" SKUs only, the ranking being pure list price with no sponsorship/affiliate weighting, and that rows expose verified_at, stale and source_url so the agent knows freshness is surfaced.

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?

Front-loads the purpose and ranking rule in the first clause, then packs the spec dimensions into a compact semicolon list. Dense but every clause carries information; slightly over-packed relative to a clean two-sentence structure.

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 10-parameter tool with no output schema, the description covers the ranking semantics, the freshness fields returned (verified_at, stale, source_url), and the key filters. It leaves out how stale rows are treated, the provider restriction and how limit/ordering behaves, but nothing essential to a first correct call is missing.

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 100%, so the schema already documents all ten parameters, making 3 the baseline. The description reinforces that vcpu/ram_gb/disk_gb are minimums rather than exact matches and that region 'eu' means EU-deployable, but it omits the limit, provider and gpu_count parameters entirely.

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?

States a specific verb and resource: rank cheapest active SKUs that satisfy a minimum spec, with the ranking rule (list price only, no sponsorship/affiliate weighting) made explicit. It implies a distinction from search_skus and get_price but never names them or contrasts the use cases directly.

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?

"Cheapest active SKUs meeting a minimum spec" implies the use case (find the lowest-priced option for a requirement), but there is no explicit when-to-use guidance, no mention of when to prefer search_skus or estimate_cost instead, and no prerequisites stated.

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