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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety is covered. The description adds genuine context beyond that: it discloses the returned payload (source, stack, stated revenue) and that the input slug should originate from search_apps. It does not cover behavior for an unknown slug, 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 short sentences, zero filler. The usage condition and payload are front-loaded before the optional example, so an agent gets the decision-relevant information first.

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 single-parameter read tool with no output schema, the description covers trigger, prerequisite, and the main return fields. Only edge behavior (unknown slug, empty result) is unaddressed, which is a minor gap.

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% – the slug parameter is fully documented with its own example ("stella"). The description's only added semantic is provenance ("from search_apps"), which is useful but minimal, so baseline 3 is appropriate.

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 the resource (app, identified by slug) and exactly what the caller gets back: source, stack and stated revenue. This clearly differentiates it from search_apps (which finds slugs) and from get_tool/compare_tools, so an agent can tell which sibling to pick without opening a 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?

"Use when you have an app slug from search_apps and need its source, stack and stated revenue" gives an explicit trigger condition and a prerequisite chain (call search_apps first). There is no explicit when-not or negative case, so it falls just short of a full 5.

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