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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower. The description adds valuable context: apps are pre-configured and normally require local setup, which explains the hosting environment and reduces user surprise. It also shows an example endpoint. It does not contradict annotations and provides extra behavioral context beyond the structured hints.

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 well-structured, front-loaded with the core action, then adds context, usage guidance, and an example. Every sentence is informative — there is no fluff. The length is justified given the need to disambiguate from several siblings and provide clear usage rules.

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 a single optional parameter, full schema coverage, strong annotations, and an output schema present, the description is complete for an agent to select and invoke the tool. It covers purpose, when to use, when not to use, and an example, leaving no critical gaps.

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% (the 'category' param is described as an optional filter with examples). The description does not add any parameter-specific meaning beyond the schema, but since the schema already fully covers it, a baseline 3 is appropriate. No extra detail is needed here.

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 clearly states the verb ('List') and resource ('open-source AI applications hosted and ready to run at AI NetCafé'), and differentiates from siblings like list_models (models vs apps) and get_app (specific app details) by emphasizing discovery of runnable tools. The concrete use cases (translating PDF, generating slides) reinforce the purpose.

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 explicitly tells when to use the tool ('Use this to find a tool for a task like...') and provides explicit exclusions with named alternatives ('Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly'). This is excellent guidance for selection.

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