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

Discloses side effect: self-initializes a scaffold if no model exists. Also notes 'No economic data from code', indicating data source limitation.

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?

Three short sentences, front-loaded with purpose. 'Free' is a bit redundant but acceptable. Overall concise.

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?

Simple tool with no parameters and output schema present. Description covers purpose and notable behavior (self-initialization). Adequate.

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?

No parameters (0 params), so baseline 4. Description adds no extra info but none needed.

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?

Clearly states verb 'Get' and resource 'active pricing model for this operator'. Differentiates from siblings like 'authority_set_pricing_model' and 'authority_reset_pricing_model'.

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?

Mentions 'Free' but does not explicitly guide when to use vs alternatives like 'authority_set_pricing_model'. No explicit when-not or exclusions.

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