explain

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

The description adds significant behavioral context beyond the annotations (readOnlyHint, etc.), such as that 'prose' uses 'Claude Haiku' and that 'position_guidance' derives from the cohort anchor. It does not contradict the annotations. However, it could further detail side effects or data dependencies.

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 well-structured with bullet points for styles and args. It is reasonably concise, though the list of styles could be more compact. No redundant sentences, but 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 the presence of an output schema (reducing need to explain returns), the description covers the input parameters and provides style-specific outputs. It lacks behavior for all styles equally and could benefit from more cross-referencing with siblings, but overall it is quite complete for a tool of this 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 0%, yet the description fully explains all three parameters: cohort_id, style (with enumerated list), and horizon (with defaults). It adds meaning beyond the schema, including the purpose of each style and default values.

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 produces 'Narrative + rankings from a stored cohort' and lists four distinct styles with brief explanations. This provides a specific verb+resource combination. However, among many sibling tools like 'narrative' and 'cohort_analyze', there is no explicit differentiation, which slightly reduces clarity.

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 gives one explicit usage suggestion: for 'filter_ranking', it says 'Use to discover conditional structure before calling `cohort` with the winning filter.' Other styles lack similar guidance, and there is no mention of when not to use this tool or alternatives beyond that single hint.

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