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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful context by explaining the frozen research-screened cohort and the specific computed outputs such as consensus, participation, agreement, effective-wallet count, and concentration. No contradiction with annotations.

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?

Two sentences with no filler. The most important action and scope are front-loaded, the output coverage is compactly enumerated, and the usage caveat earns its place as guidance for responsible use.

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 usefully lists key returned metrics, but does not describe return structure or formatting. This is a minor gap since the tool's purpose, parameters, and safety profile are otherwise well covered.

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 has 100% description coverage for all six parameters, so the schema carries the semantic load. The description adds context about the focus symbol and optional recent fills, but does not need to explain parameters in detail. Baseline 3 is appropriate.

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 starts with a specific verb and resource: 'Read current public Hyperliquid positions for a frozen, research-screened whale cohort.' This clearly distinguishes the tool from general market-data siblings like orderflow_read or market_relationships by specifying both the data source and the cohort.

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 explicitly states when to use the tool: 'Use this for whale/smart-money positioning as one evidence family.' It also gives an exclusion: 'never as a standalone signal.' It does not name sibling alternatives, but the usage context and limitation are clear enough for an agent to decide.

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