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

Beyond the read-only and open-world annotations, it discloses the 100-address result cap, cohort-specific metric meanings and USD units, and that fetched_at reflects when the cohort was last computed. These details set useful expectations about result size, interpretation, and freshness.

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?

Three dense sentences front-load the result and then cover the prerequisite and downstream workflow. Nearly every clause adds actionable information, and the examples remain relevant without becoming filler.

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 one-parameter read with an output schema and read-only annotations, this covers scope, limits, data meaning, freshness, cohort discovery, and next steps. Nothing material is missing for selecting and invoking the tool correctly.

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%, and the description largely repeats that name should come from list_cohorts rather than adding new syntax or validation details. The ranking-metric explanations concern returned values rather than the parameter itself, so the high-coverage baseline applies.

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 opening identifies the exact resource—wallet addresses for one named cohort—and the description distinguishes this from list_cohorts, which supplies cohort names and selection logic, and from downstream wallet-inspection tools. The 100-address cap and cohort-specific ranking values further sharpen its scope.

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 gives an explicit prerequisite—call list_cohorts first—and explains that this provides valid names and cohort selection criteria. It also names three downstream tools and explains when to feed the returned addresses to them, providing a concrete workflow.

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