simulate_price_change

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), and the description goes beyond them by disclosing the mechanism ('uses the SKU's estimated elasticity') and warning that the output is model-based and carries assumptions worth reading. That is genuine behavioral context: the numbers are projections, not measurements. It does not mention precision, edge cases (missing elasticity for a SKU), or failure modes.

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, front-loaded with what is computed, followed by the mechanism and the caveat. Nothing is redundant and every sentence carries information an agent would otherwise have to guess.

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 presence of an output schema means return values need not be described, and the description covers inputs, mechanism, and the caveat about assumptions. The remaining gap is units/currency for new_price, which matters for a pricing tool and is documented nowhere.

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 0% – both parameters carry only bare titles. The description clarifies what new_price means (the price the SKU 'moves to', i.e., a target price, not a delta or percentage) and that the sku drives an elasticity lookup. It does not state currency/units for new_price or expected SKU format, so it only partially compensates for the coverage gap.

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 verb 'Project' plus the concrete outputs (weekly units, revenue, gross margin) and the trigger condition (a SKU moving to new_price) make the tool's function unmistakable and clearly different in kind from siblings like get_price_elasticity or check_price_guardrails. It stops short of naming a sibling to route away from, so it is clear but not fully differentiated in text.

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?

Usage is only implied: the word 'Project' signals a what-if simulation, so an agent can infer this is a pre-change evaluation rather than a data lookup. There is no explicit statement of when to use this instead of get_price_elasticity (the input side) or check_price_guardrails (the validation side), and no stated prerequisites.

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