add_percent_of_total

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

No annotations are provided, so the description carries the burden for behavioral disclosure. It mentions 'Generate' implying a mutation, but does not state side effects, reversibility, permissions, or return values. It only repeats the scope options which are already in the parameter schema, adding little value beyond that.

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 short sentences, front-loaded with the main purpose. Every sentence earns its place with minimal redundancy, making it 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?

The tool is a mutation with no annotations and no output schema. The description fails to mention what is returned, any prerequisites, or side effects. It is too sparse for a tool that modifies the model, leaving significant gaps in understanding.

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 covers 80% of parameters with descriptions (table, baseMeasure, dimension, scope). The description adds no extra parameter meaning beyond restating the scope options already present in the schema. Baseline 3 is appropriate since 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 a specific action: 'Generate a percent-of-total measure for a base measure over a dimension.' This is a specific verb+resource and distinguishes it from similar siblings like add_percent_of_parent or add_measure by explicitly mentioning percent-of-total and the dimension context.

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 context on what the tool does but does not explicitly state when to use it versus alternatives. It does not mention exclusions or provide alternative tool names. The purpose is clear enough to imply usage, but there is no explicit guidance.

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