add_running_total

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It implies a write operation (adds a measure) but does not state side effects, return values, or whether it modifies the model irreversibly. The description only covers input modes, not the behavioral impact.

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 deliver the core purpose and the two usage patterns with no wasted words. The first sentence states the purpose, the second explains the alternatives, making it front-loaded and easy to scan.

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?

The description explains the main input distinction but omits important context such as the role of baseMeasure (the measure to accumulate), the need for a home table, and expected return value (e.g., the new measure's name). Given the tool's moderate complexity and lack of output schema, this is a notable gap.

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 provides descriptions for 83% of parameters, but the description adds crucial semantics by explaining the alternative relationship between dateTable/dateColumn and sortColumn, and what each mode produces. This goes beyond the schema's simple 'omit when using sortColumn' hint.

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 explicitly states 'Generate a running-total measure' with a clear verb and resource, and outlines two distinct modes (date cumulative vs generic/Pareto). This distinguishes it from other measure-creation tools like add_rank_measure or add_moving_average, even without naming them.

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 clear conditional guidance: 'Provide dateTable+dateColumn for a date cumulative, OR sortColumn ... for a generic / Pareto running total.' This tells the user which parameters to use based on the desired output. However, it does not explicitly mention when not to use this tool or name alternative tools.

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