pulse_cohort_recent_alpha_concentration

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

No annotations are provided, so the description bears full responsibility for disclosing behavior. It lists the return fields (band, wallet count, band PnL, % of tier PnL, tier total wallets), indicating a read-only analysis. It does not mention data freshness, rate limits, or computation details (e.g., how percentile bands are defined), but the core behavior is sufficiently described for a non-mutative tool.

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 three sentences with zero wasted words: first sentence defines the tool, second lists return fields, third provides a concrete usage example. Information is front-loaded and every sentence earns its place.

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?

Given no output schema, the description adequately lists the return fields and explains the purpose. However, it omits context about the meaning of 'recent' in the cohort, how percentile bands are computed, or any prerequisites (e.g., does the tier need to be populated?). Still, for a focused analysis tool with many siblings, it provides enough context for an agent to select and invoke it 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?

The input schema already has 100% parameter descriptions, covering tier, tierType, and useToonFormat. The tool description does not add additional semantic information about these parameters (e.g., clarifying how tierType affects the cohort selection). Baseline 3 is appropriate since the schema handles the definition; the description adds no extra value for parameters.

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 clearly states the tool measures profit concentration within a recent-tier cohort using percentile bands and PnL shares, with a concrete use case example ('within the hot apex cohort, do a few wallets carry everything?'). This is specific, actionable, and distinguishes the tool from sibling cohort tools that likely provide aggregate stats or positions.

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 provides a clear use case example, implying when to use this tool (to analyze concentration in a tier cohort). However, it does not explicitly state when not to use it or compare it to alternatives like pulse_cohort_summary or pulse_market_concentration. The example compensates partially, but explicit exclusions would strengthen it further.

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