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

Annotations already cover safety (readOnlyHint=true, openWorldHint=false), and the description adds real value by describing the shape of the response – currency, quota limits, and the URL where the plan can be changed. It does not discuss ordering, pagination, or whether all plans or only the current one are returned, which keeps it from a 5.

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 sentences, front-loaded with what the tool returns and followed by the interaction policy. Both sentences carry weight, though the second one's example ('a smaller AI model tier') is slightly more than strictly needed.

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 one-parameter read tool with annotations covering safety and no output schema, the description supplies the missing piece – what the response contains. Nothing critical for correct invocation is absent, though a note on which plans are returned would round it out.

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% and the single app_id parameter is already documented as coming from list_apps. The description adds no further semantics about the parameter, so the baseline 3 applies.

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 ('Lists Kite's plans') and enumerates what comes back: prices in USD, limits such as credits per month, and the plan-change page. That is enough to separate it from neighbours like get_usage or list_apps, though no sibling is named explicitly.

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 a clear trigger ('when the app is near a limit or the user asks') and an explicit pre-step ('first suggest ways to need less, like a smaller AI model tier'). It stops short of naming the alternative tool to consult (e.g. get_usage) for checking limits, so the routing is implied rather than fully explicit.

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