Cohort Summary

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

Annotations already declare the tool read-only, open-world, idempotent, and non-destructive, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: response payloads use legacy slugs like money_printer and leviathan, and each tier exposes wallet count, avg PnL, avg win rate, and total volume.

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 core purpose is front-loaded in the first sentence, and the remaining sentences add concrete output details and a necessary prerequisite call. The tier lists are fairly long but earn their place by setting expectations about the response taxonomy.

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?

Even without an output schema, the description explains what the response contains: tier categories, per-tier metrics, and legacy slug naming. It also covers the related global-stats prerequisite. Minor gaps remain around the exact top-level payload shape and the full size-tier list, but the tool has only one optional boolean parameter and is otherwise well-specified.

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 fully describes the single optional useToonFormat parameter with its default and meaning, so schema coverage is 100%. The description adds no additional parameter-level semantics beyond what the schema already provides, which matches the baseline for full schema coverage.

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: 'Get behavioral cohort analysis across every tracked wallet on Hyperliquid.' It further differentiates the tool by enumerating the PnL and size tier categories it returns, which distinguishes it from sibling cohort tools like pulse_cohort_positions or pulse_style_distribution.

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 scope clear ('across every tracked wallet') and advises calling pulse_global_stats first when the total tracked-wallet count is needed. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it, so usage guidance is mostly implied rather than explicit.

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