explain

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

With no annotations, the description carries the full burden. It discloses the axiom check (baseline contributions = prediction) and warns that a counterintuitive sign on a low-importance driver indicates under-sampling. It also reveals that the output includes starting_point and price_drivers with both dollar and percent effects. This is substantial behavioral disclosure, though it omits error handling or rate limits.

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 longer than average (about 10 sentences) but well-structured: purpose first, then output structure, then usage guidance. Every sentence adds value, and the reading instructions are actionable. It is not bloated, though it could be trimmed without losing meaning. Front-loaded and logically organized.

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 complexity (nested objects, output schema present, but schema descriptions absent), the description covers the output structure and interpretation thoroughly, but it does not specify constraints on features (e.g., must match training features), behavior if top_n exceeds driver count, or what happens if the axiom check fails. It also lacks any note on model_id validity. The output schema covers return values, but parameter details and edge cases are incomplete.

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 coverage is 0%, so the description must compensate. It explains top_n ('controls how many top drivers are highlighted, default 3') but does not describe features or model_id at all. Features is implied to be the specs/suppliers, but the format and required fields are undefined. model_id is never mentioned. Partial compensation, but key parameters remain underdocumented.

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 opens with a specific verb and resource: 'Explain what is driving a part's predicted price, spec by spec.' It clearly distinguishes from siblings like predict by focusing on the explanation of drivers rather than the prediction itself. The purpose is unmistakable.

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 when to use this tool: when you need a business-ready explanation to quote directly, and it gives detailed instructions on how to interpret and present the output (sign-check, quote high-importance drivers, avoid jargon). However, it does not explicitly contrast with siblings like predict or what_if, nor state when not to use it. The context is clear but exclusions are not stated.

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