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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds the scope constraint (app screens, not conversations) and notes the enriched picker payload (logo, badge, summary, default). It gives no pagination, ordering, or auth context, so this is a modest value-add.

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 tight sentences, with the scoping constraint front-loaded before the functional description. No sentence is wasted.

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?

An output schema exists, so return values need not be explained, and the description still hints at the payload shape. Adequate for a single-enum read tool; only minor gaps remain around ordering or which models are included.

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?

Only one parameter, and schema coverage is 100% with the enum values already documented in the schema. The description says 'for one kind' but adds no format or semantics beyond what the schema provides, so the baseline of 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+resource (answers list_models for one kind) and immediately names the sibling it differs from, plus the scope ('the Veltake app's own screens only'). An agent can distinguish this from list_models without opening either schema.

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 when-to-use boundary ('for the Veltake app's own screens only') and an explicit when-not ('not for a conversation'), and implicitly routes conversational model listing to list_models by contrasting the two. It stops short of explicitly naming list_models as the alternative for non-app contexts.

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