add_dynamic_segmentation

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does disclose the interval semantics ('left-closed, right-open bounds') and the logical result (count of entities), but it does not clarify what the tool actually returns to the caller (e.g., a measure object or string) or whether it modifies existing measures. This ambiguity leaves room for misunderstanding the tool's direct output and side effects.

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 two sentences, front-loaded with the main purpose, and contains no redundant words. Every clause contributes to either the action or the algorithm, making it highly efficient and easy to parse.

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 tool's complexity and lack of annotations/output schema, the description gives a solid overview but omits practical details: what the tool directly returns, how the 'selected segment' is specified, and any limitations (e.g., handling of nulls or out-of-range values). These gaps make it harder for an agent to invoke the tool confidently without additional guesswork.

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 schema already describes 7 of 8 parameters, so baseline is 3. The description adds important context by explaining the 'disconnected band table' and 'left-closed, right-open bounds,' which clarifies the roles of lowerColumn and upperColumn. It also connects measure and granularityColumn to the classification logic, enriching understanding beyond the individual schema descriptions.

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 identifies a specific action ('Generate a dynamic-segmentation measure') and explains the mechanism ('classifies an entity by a measure value against a disconnected band table') and the output ('Returns the count of entities in the selected segment'). This clearly distinguishes it from sibling tools like add_dynamic_topn or add_abc_classification by focusing on band-table-based segmentation.

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 implies a clear use case—dynamic segmentation using a band table—but does not explicitly state when to choose this over alternatives or what prerequisites exist (e.g., whether the band table must be sorted). While the context is useful, it lacks explicit exclusions or alternative tool references.

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