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

Annotations already declare read-only, idempotent, open-world, non-destructive behavior. The description adds useful context beyond annotations: it describes the returned fields, the overall lean comparison, and that omitting side costs 2 API calls, which helps an agent reason about invocation cost.

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 question and then covers output fields and API-call behavior. It is dense but efficient, with no obviously wasted sentence, though the first sentence is somewhat long and colon-heavy.

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?

With no output schema, the description helpfully explains the returned metrics such as long/short notional, net USD, share long, wallet count, and cohort lean. It covers the essential behavior for a read-only analytics tool, though it does not describe response ordering or error conditions.

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 description coverage is 100%, so every parameter is already documented in the schema. The description reinforces the meaning of side omission by noting the 2-call behavior without a side, but it adds little parameter detail beyond what the schema provides.

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 resource and ranking task: which coins a given trader cohort is most long and short on across every market. It frames the exact question it answers and distinguishes the output from a single-market snapshot by mentioning the cohort's overall lean across all markets.

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 example question implies when to use the tool, but the description does not explicitly state alternatives or when not to use it versus siblings like cohort_positioning or top_positions. Usage is clear enough from the framing but not fully guided.

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