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

The description adds useful behavioral detail by explaining that hard rules produce 'block', the soft rule produces 'warn', and otherwise 'pass'. Annotations already confirm this is a read-only, idempotent check, so the description adds outcome semantics rather than repeating safety metadata. It does not describe error behavior or policy-source details, but it is solid for a read-only guardrail check.

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 short, front-loaded with the core action, and immediately explains the possible outcomes. Every sentence contributes directly to understanding the tool.

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 has an output schema, so return values do not need full explanation. However, with 0% input schema description coverage, the description leaves gaps in parameter semantics and does not distinguish this check from closely related sibling tools.

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%, so the description must compensate for the two undocumented parameters. It mentions the proposed price conceptually but does not explain SKU format, price currency, units, or expected value ranges, leaving parameter semantics largely to inference.

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 uses a specific verb and resource: checking a proposed price against the pricing policy. It clearly explains the tool's core function, but it does not explicitly distinguish it from similar siblings such as check_price_ladder or simulate_price_change.

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 implied by the name and description: use this to evaluate a proposed price before making a price change. However, there is no explicit guidance on when to use it versus simulate_price_change or check_price_ladder, nor are prerequisites or exclusions stated.

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

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

Annotations already establish the safe read-only, idempotent, closed-world profile, so the bar is lower. The description still adds real behavioral value by naming the two anomaly conditions it flags, which tells the agent what the output represents beyond a generic 'check'.

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, zero filler, with the core action front-loaded and the detection criteria following as supporting detail.

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?

An output schema exists, so return-value explanation is unnecessary, and the description covers the detection logic well. The remaining gap is the ambiguous category parameter, which is a minor but real omission for a one-parameter tool.

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?

Only one parameter and schema description coverage is 0%, so the description must carry the burden. It says the check runs 'in a category' but gives no indication of the expected format (name vs. ID, taxonomy level), leaving the sole parameter under-specified.

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?

States a specific verb ('Find') and resource ('price-ladder breaks') and then defines the detection rules concretely (bigger packs cost more per ounce, higher tiers priced at or below lower tiers). It is clearly distinguishable from siblings like check_price_guardrails or get_price_elasticity, though it never names an alternative explicitly.

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: it's an analysis/audit tool scoped to a single category. There is no explicit statement of when to reach for this versus check_price_guardrails or simulate_price_change, and no prerequisites or exclusions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety profile is covered. The description adds that this is an aggregate per-SKU comparison producing an indexed score versus a target band, but it says nothing about how SKUs outside a band are treated or what the computation needs to run.

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?

A single front-loaded sentence with no filler; the comparison logic and the target-band criterion both earn their place. It is arguably too terse for a tool whose only parameter is undocumented, but there is no structural waste.

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?

With an output schema present and read-only annotations, return values and safety need no explanation. However, the required `category` argument and the scope of 'each SKU' (all products in that category?) are left unstated, leaving the agent under-informed before invocation.

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% and the single required parameter `category` is never mentioned in the description. The agent must guess whether it is a product category, a filter scope, or an enum-like value, so the description does not compensate for the schema 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 description names a specific verb (compare) and a precise comparison basis: each SKU's price vs. the median competitor price, evaluated against its tier's target band. That is enough to distinguish it from siblings like check_price_ladder or check_price_guardrails, though it never states the scope the required `category` argument implies.

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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of alternatives such as check_price_ladder or get_price_elasticity. The phrase 'against its tier's target band' hints at context but leaves the agent to infer when this analysis is the right call.

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

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

Annotations already declare readOnly, idempotent, closed-world behavior; the description adds the meaningful trait that the estimate is derived from a 52-week historical window and that it is a statistical output with a confidence interval and confidence label. It does not say what happens when a SKU has insufficient history, which is the main remaining behavioral 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?

Two sentences, purpose front-loaded, nothing redundant. The second sentence on returns is arguably covered by the output schema, but it is short and useful.

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 single-parameter read-only tool with an output schema, the description covers what is computed, the data window, and the shape of the result. It stops short of stating data-sufficiency requirements or SKU-level scope, which matters for a statistical estimator.

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?

Only one parameter exists and schema description coverage is 0%, so the description must carry the burden. It implies the input is a SKU ('a SKU's unit sales') but adds no format, identifier convention, or granularity (per channel? per region?) beyond the property name 'sku'.

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?

States a specific verb (estimate) and resource (price elasticity / sensitivity of unit sales to price) with the data basis (52 weeks of sales). It is distinguishable from siblings like simulate_price_change by framing itself as an estimation from historical data rather than a forward simulation, though it never names or contrasts those siblings explicitly.

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 'from 52 weeks of sales' framing suggests this is the historical-measurement tool and not the what-if tool, but there is no explicit when-to-use, when-not-to-use, or reference to simulate_price_change / check_price_guardrails as alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful content-level disclosure (that a pricing policy accompanies the SKU rows), but says nothing about ordering, completeness, pagination, or whether the category filter is an exact match.

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 short lines, front-loaded with the primary purpose before the parameter note; every clause carries information. Minor waste from the trailing line break and the slightly list-like formatting.

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?

A read-only, single-parameter listing tool with an output schema that already documents the return shape. The description covers purpose, return contents, and the one parameter's allowed values, so an agent has everything needed to call it correctly.

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% and the schema declares category as an unconstrained anyOf string/null with no enum, so the description is the sole source of the allowed values whole_bean, ground, cold_brew, and confirms it is optional. It does not state case sensitivity or the behavior when omitted (list all), keeping it off a 5.

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?

States a specific verb ('List') and resource ('SKUs') and enumerates the returned fields (tier, pack size, price, unit cost, pricing policy), so the agent knows exactly what comes back. It does not explicitly contrast with siblings like get_price_elasticity or competitor_price_index, but the resource-level purpose is distinct enough to disambiguate.

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 contains no when-to-use guidance, no prerequisites, and no exclusions relative to the sibling pricing tools. The only context given is that the category parameter is an optional filter, which is parameter semantics rather than usage routing.

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

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.