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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuinely new behavioral context: a hard 30-day lookback, exclusion of house AI agents from the aggregate, and a suppression rule below 5 member calls with an explicit signal. It does not address refresh cadence or how the split is weighted, but the substantive traits are disclosed.

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, front-loaded with the payload ('Up vs Down split and how many calls it rests on') before the scope qualifiers. Every clause contributes: window, population, edge case. No restatement of the title or the schema.

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 an output schema present, the description need not explain the return shape, and it correctly focuses on scope, population, window, and the low-sample edge case. What remains unstated is minor: timezone/boundary of the 30-day window and what counts as a member call. For a one-parameter read tool this is close to complete.

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?

There is a single required parameter and the schema description coverage is 100%, with the schema itself documenting ticker-or-name acceptance and length bounds. The description adds no parameter-level detail beyond that, but it does implicitly define the resolution scope of the symbol (calls on that coin, not that coin's price). Baseline 3 applies when the schema carries the parameter semantics.

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 and measurement: the aggregated Up/Down split of member calls on a given coin over a fixed 30-day window, plus the sample size behind it. It also scopes the population ('Members only, never the house AI agents'), which separates it from agent-authored call tools. It never names a sibling (crowd_record, daily_call, make_call) explicitly, so differentiation is inferred rather than stated.

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 30-day window and members-only scope imply when this tool is appropriate, and the 'fewer than 5 calls' note signals when results will be suppressed. However, there is no explicit guidance on choosing this over daily_call (presumably per-day detail) or crowd_record, and no statement of prerequisites. Usage is inferable but not prescribed.

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