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

With no annotations, the description carries the full burden and does it well: it discloses the 7-day window, the attested-tier-only filter, the exclusion of classified monitor/recon data, and the fact that this is a public read. It also adds cost/rate-limit and latency expectations, which is exactly the behavioral context 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?

The description is front-loaded with the core purpose, then organized into use-case bullets, return fields, cost, and latency. Each section earns its place and no sentence is filler, despite the length.

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 stats tool with no output schema, this is complete: it defines the data scope, lists every return section, notes exclusions, and states cost/latency. An agent has everything needed to decide whether to call it and what to expect.

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 takes zero parameters and the schema is an empty object, so there are no parameter semantics to explain. The description instead clarifies the implicit data-scope filters, such as attested tier and 7-day window.

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 names a specific resource ('Conduct Log Commons'), a clear read operation, and the exact data scope: agents knocking on attested tollbooth sites in the last 7 days, their behavior when offered paid access, and would-have-paid amounts. It also draws boundaries (excluded first-party monitors and recon) that distinguish this stats view from nearby tollbooth_export and tollbooth_sites.

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?

Two explicit 'Use this tool when' bullets define concrete triggers: understanding AI crawler behavior at paywalls and retrieving current commons membership/export pointers. There is no explicit 'don't use' or named alternative, but the context is strong enough to route an agent.

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