add_abc_classification

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool generates a dynamic measure and a calculated class table, but it does not mention whether this modifies the model, overwrites existing objects, requires specific permissions, or has any side effects. For a mutation-type tool without an output schema, this is a significant gap.

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 purpose, and every clause earns its place. It concisely explains the output and threshold semantics without any filler or repetition of schema details.

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?

For a tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It mentions the generated artifacts but does not describe return behavior, default naming, prerequisites (e.g., numeric value measure, existing entity table), or potential conflicts. This leaves substantial ambiguity for an agent deciding how to call it.

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 86%, which sets a baseline of 3. The description adds meaningful semantics for aThreshold/bThreshold by explaining they are cumulative-share cut-offs and that the rest fall into C, which is not fully explicit in the schema. This helps the agent understand the classification logic beyond mere parameter names.

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 leads with a specific verb 'Generate' and identifies the resource 'ABC (Pareto) classification'. It further specifies the tangible outputs (a dynamic class measure plus a calculated class table), which clearly distinguishes it from generic add_* tools like add_measure or add_calculated_table. This is not a tautology and provides concrete purpose.

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 usage for ABC/Pareto analysis by explaining the threshold cut-offs, but it does not explicitly state when to prefer this tool over alternatives such as add_dynamic_segmentation or add_measure. No when-not scenarios or alternative tool references are provided, so the guidance is only implied.

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