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

Annotations already establish this as a safe, read-only, idempotent operation. The description adds useful behavioral context beyond the annotations: Substack computes the data in the background, and a new publication may show empty lists for a while.

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 front-loaded with the core purpose and then enumerates the returned data. It is dense but well structured, and every clause contributes useful selection or interpretation context.

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?

There is no output schema, so the description must explain return values, and it does: subscriber counts by geography, total country/state counts, and overlapping publications. It also covers the important caveat about delayed computation for new publications.

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%, so the schema already documents both parameters and defaults. The description still adds value by giving ISO code examples such as us, gb, ny, and ca, and by clarifying that paid_only restricts counts to paid subscribers.

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 states a specific verb and resource: returns audience insights about where subscribers live and which publications share readers. It distinguishes this from sibling analytics tools by naming the exact dimensions returned: subscriber counts by country or US state, paid/all counts, countries/states with subscribers, and overlapping publications.

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 makes the usage context clear by locating the data under Analytics > Statistics > Audience and explaining both region modes. It does not explicitly name alternatives such as get_publication_stats or list_subscribers, but the scope is clear enough for an agent to choose it for geographic audience and overlap data.

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