live_cohort_bias_history

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

No annotations are provided, so the description bears full responsibility. It correctly identifies the tool as read-only (historical retrieval) and describes the output as hourly net-bias snapshots. It lacks details on error cases, rate limits, or data availability guarantees, but for a simple query tool, this is acceptable.

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 long, front-loads the core function, and provides examples efficiently. It is concise without being terse, though it could be slightly more structured (e.g., seperating purpose and usage more clearly).

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 5 parameters and no output schema, the description adequately states the return format (hourly net-bias snapshots). It does not explain how to interpret bias values or mention the default tierType or useToonFormat, but the schema handles those. Overall, it is sufficient for the tool's complexity.

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 parameters are well-documented in the schema itself. The description adds minimal additional meaning beyond the schema (e.g., it implies the 'hours' parameter via 'hourly' and 'over time', but does not explain 'tierType' or 'useToonFormat'). A score of 3 reflects that the schema does the heavy lifting.

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 it retrieves historical cohort bias data for a specific coin and provides example user queries, making the purpose specific. However, it does not distinguish this tool from the sibling 'pulse_cohort_bias_history', which may serve a similar role.

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 gives use cases with natural language queries, e.g., 'were smart-money cohorts accumulating or exiting?', helping agents select when to invoke. It does not, however, mention when not to use it or contrast it with alternatives like 'live_cohort_bias' for current data.

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