PackValue Tools

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

No annotations are present, so the description is the only behavioral signal. 'Calculate' implies a pure read-only computation and names the output values, but does not mention edge cases (e.g., pay_units=0), rounding, or return format. This is acceptable for a simple calculator but not fully transparent.

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 sentence that front-loads the computation and the domain, with no filler or repeated title content.

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 simple 4-parameter calculator with no output schema, the description names all returned quantities and the applicable promo types, which is enough for an agent to call it. It lacks output formatting and edge-case guidance, but these are minor for this 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?

Schema descriptions are absent (0% coverage), leaving parameter semantics to names and the description. Names like take_units, pay_units, regular_unit_price and currency are self-explanatory, and the promo wording maps X/Y to the unit parameters. However, no description clarifies relationships (e.g., whether take_units must exceed pay_units) or the currency default, so the description only partially compensates.

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 clear verb ('Calculate'), explicit output set (total paid, effective unit price, discount), and bounds the domain to buy-X-pay-Y/take-X-pay-Y promotions. It distinguishes itself from sibling normalization/comparison tools by naming the promotion type, though it leaves overlap with calculate_real_discount implicit.

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 'for buy-X-pay-Y or take-X-pay-Y promotions' clause gives a clear applicability condition. It does not name sibling alternatives or state exclusions, so an agent must infer when not to use it, but the target scenario is explicit.

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?

With no annotations, the description must carry the full behavioral burden. It lists the cost components, but does not disclose the calculation formula, how discount and tax are applied, whether shipping is per-order or per-unit, or how currency is used. The phrase 'explicit discount' hints that discount is a direct input, but the actual behavior remains ambiguous.

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?

One concise sentence, front-loaded with the core operation and followed by the cost components. There is no filler or redundant elaboration beyond the slight rewording of the title.

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 6-parameter tool with no annotations and no output schema, the description is too thin. It omits the formula, the role of quantity, the currency parameter, and the return value. An agent would have to guess important behavioral details such as whether tax is computed on the discounted subtotal.

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. It identifies shipping, tax, and discount as cost components, which adds meaning beyond the bare property names. However, it does not explain the currency parameter, nor whether tax/discount are absolute amounts or rates, so compensation is incomplete.

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 states a specific verb ('calculate') and a specific resource ('delivered cost per unit'), then names the included components (shipping, tax, explicit discount). This makes the operation distinct from siblings like calculate_real_discount or normalize_pack_value, which address different calculations.

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 gives no guidance on when to prefer this tool over alternatives, nor does it state exclusions or prerequisites. While the name implies it is for delivered unit cost, there is no explicit 'use this when...' context, and the sibling tools are not mentioned.

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?

No annotations are provided, so the description carries the behavioral disclosure burden. It clearly describes a pure calculation involving a concentrate and a zero-cost diluent, but it does not disclose the return format, units, or exact meaning of 'prepared yield' and 'effective cost.'

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 a single focused sentence with no filler. The key outcomes, 'prepared yield' and 'effective cost,' are front-loaded before the dilution method.

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 is a simple four-parameter calculator, but there is no output schema and no description of the return structure or exact formula. An agent can likely infer how to call it from parameter names and the description, but the ambiguity around output semantics is a notable gap.

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?

With 0% schema description coverage, the description must compensate for parameter meaning. It clarifies that added_parts_per_concentrate_part means 'added parts of water or another zero-cost diluent' and connects concentrate volume/price to yield/cost, but it does not explicitly define currency, units, or the calculation formula.

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 operation (calculate) and a specific resource (prepared yield and effective cost after diluting a concentrate). The dilution focus clearly distinguishes it from the sibling retail-discount calculators such as calculate_real_discount and compare_pack_values.

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 guidance about when to use this tool versus its siblings, nor any mention of exclusions or alternative tools. The intended context is only implied by the dilution scenario rather than 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?

With no annotations, the description carries full behavioral burden. It does not disclose whether this is a pure calculation (no side effects), how outputs are formatted (e.g., percentage as a decimal vs. whole number), rounding behavior, or any edge cases. For a calculation tool, these details matter for correct interpretation of results.

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 a single, concise sentence that front-loads the core purpose. It contains no fluff, but it could be slightly more structured to break down inputs and outputs.

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 simple calculation tool with no output schema and no annotations, the description is incomplete. It does not specify the return format, how to handle different currencies, or any caveats. An agent has to infer or test to get correct results, especially regarding output representation.

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 mentions 'original and final prices' which maps to two of the three parameters, but it does not describe the optional 'currency' parameter or any constraints. The description adds minimal value beyond the raw schema field 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 states a specific verb ('Calculate') and the exact outputs ('absolute savings' and 'effective discount percentage') from two inputs ('original and final prices'). This clearly distinguishes it from siblings which handle buy X pay Y, unit cost, dilution, pack comparisons, and normalization.

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 description—use this when you have original and final prices and need savings/discount. However, no explicit alternatives or exclusions are provided, and it doesn't mention scenarios where siblings would be more appropriate, so it lacks the proactive routing seen in higher-scoring definitions.

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?

With no annotations, the description carries the full burden, and it does disclose that the comparison accounts for shipping, tax, discount, yield, and dilution adjustments. It also notes the compatibility requirement for dimensions and currency. However, it does not describe what the tool returns (e.g., ranking, best offer, savings) or any side effects/assumptions, leaving an important 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?

The description is a single sentence with no filler, front-loading the essential action and the offer-count range. The list of adjustments is compact and informative. It loses one point because the information is densely packed without any structural signposting for parameter rules.

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 a nested offers schema, no annotations, and no output schema, so the description needs to explain both invocation requirements and expected results. It explains the input scope but omits the output shape, comparison criteria, and behavior when offers are incompatible. An agent would still have to guess what 'compare' yields.

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. It lists several financially meaningful parameters (shipping, tax, discount, yield, dilution), giving the agent some semantic grounding. It does not explain the role of text, label, price, or currency, nor how the adjustment fields interact, so compensation is partial.

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 clearly states a specific verb and resource: comparing 2 to 25 shopping or procurement offers. It also names the included adjustments (shipping, tax, discount, yield, dilution), which distinguishes it from more general calculation tools. However, it does not explicitly contrast itself with the sibling tools, so it stops short of a 5.

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 the tool (when comparing multiple offers) but gives no explicit guidance about when to prefer a sibling tool such as normalize_pack_value or calculate_real_discount. There are no exclusions, prerequisites, or alternatives mentioned.

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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It usefully states that the tool is deterministic and uses no LLM for calculation, but it does not mention return shape, error handling, or how optional financial parameters affect the result.

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 concise sentences with the purpose front-loaded and a concrete example. The deterministic note adds useful information without unnecessary verbosity.

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 nine parameters, no output schema, and no annotations, this description is too thin. An agent would struggle to know how to populate most optional parameters or what the normalized result looks like, and sibling differentiation is missing.

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 there are nine parameters, so the description must compensate. It illustrates the text parameter with examples like '6x330 ml' and implies price, but leaves tax, discount, dilution, shipping, currency, yield_pct, and label semantically unexplained.

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 clearly states the operation: normalizing a product pack such as '6x330 ml' into comparable quantity and effective unit price. It describes a specific transformation and resource, though it does not explicitly name sibling tools to differentiate them.

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: use this when a raw pack string and price need to be converted into a normalized quantity and unit price. However, there is no explicit guidance about when to prefer this over siblings like compare_pack_values or calculate_delivered_unit_cost.

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