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

No annotations are supplied, so the description carries the full burden. It implies a non-mutating lookup by saying it returns "priced options," but says nothing about whether a quote is persisted, whether prices are live or indicative, currency assumptions, or how many options come back. For a tool with a required free-text need and defaults on qty/ships_to, this is thin.

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 short sentences, zero filler, with the purpose front-loaded and the usage condition immediately after. Nothing is repeated from the schema.

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 4-parameter tool with no annotations and no output schema, the description covers the core intent but leaves gaps: the shape of the returned "priced options" is undefined and ships_to is undocumented. Adequate to attempt a call, not enough to predict the result.

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 0%, so the description must compensate. It maps meaning onto need, qty and budget_max ("their budget"), but ships_to and its "US" default are never mentioned, and no format hints are given for need. Partial compensation only.

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?

"Turn a need + quantity into priced options" states a specific transformation (unstructured need -> priced options) rather than restating the name. It is distinguishable from get_product/search_products by framing the input as a need rather than an identifier, though it never names a sibling to contrast with.

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?

Gives an explicit trigger condition: use it when the human does not know the model number, only the need and budget. That is a real when-to-use rule. It stops short of naming the alternatives (search_products, recommend_for_answer) or stating when not to use it.

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