apple_podcasts_charts_rankings

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

With no annotations, the description carries the full burden. It discloses the source (modern podcasts.apple.com charts page) and the coverage of algorithms/types, but does not mention return format, pagination, limits, or any edge-case behaviors. It clarifies the operation is a retrieval (read-like), but lacks deeper behavioral disclosure.

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 two sentences, front-loaded with the primary action and target. Every clause adds value — the first sentence captures purpose, the second adds source detail and differentiates from the sibling tool. No wasted words or redundancy.

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?

The description explains the key dimensions (algorithm, type, genre) and the source, but does not describe the return structure or pagination behavior. With no output schema and no annotations, the agent receives only partial context; it knows what the tool does but not what it returns for various input combinations. Still, it is adequate for a simple chart retrieval.

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?

The input schema covers 100% of parameters, giving a baseline of 3. The description adds the key insight that chart algorithms are 'crossed with' entity types, explaining the combinatorial relationship between `chart` and `type` parameters, which is not explicit in the schema. This enriches parameter understanding.

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 tool retrieves Apple Podcasts chart rankings by algorithm, type, and genre. It names specific algorithms and entity types, and explicitly contrasts with the legacy RSS-based sibling endpoint, making its scope and differentiation unambiguous.

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?

It provides clear context by noting this is a 'richer, differently-sourced capability' than the legacy `/apple-podcasts/charts` endpoint, guiding the agent to prefer this for modern charts. However, it does not spell out exhaustive when-to-use/when-not-to-use scenarios or list other alternatives.

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