What is actually in your library

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond those hints: on Mac, play data often is absent because listening progress does not sync, so the tool may report unusable data. This is exactly the kind of caveat an agent needs.

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 with no filler. The first sentence front-loads what is counted; the second explains when to call it and warns about a platform-specific limitation. Every sentence carries meaningful guidance.

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 zero-parameter, read-only statistics tool, the description fully covers what it returns, when to use it, and a critical data-quality caveat. No output schema is present, but the description lists the counts sufficiently for an agent to interpret results.

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 tool has zero parameters, so there is no parameter semantics burden. The description still clarifies what the output counts represent, which is useful since there is no output schema.

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 counts specific metrics for the local library (shows, episodes, saved/bookmarked/downloaded items), and the title reinforces the distinct purpose. This distinguishes it from sibling tools like search_library or list_recent_episodes that list or search rather than summarize.

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?

Explicit guidance is given: 'Call this before drawing any conclusion about listening habits,' and the Mac-specific caveat helps agents decide when the data may be unreliable. It does not explicitly name alternative tools, but the context is clear enough for selection.

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